Computational methods applied to syphilis: where are we, and where are we going?

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

1. Introduction

Syphilis is an infectious disease caused by Treponema pallidum subsp. pallidum (T. Pallidum) infection that can be sexually transmitted (Acquired Syphilis—AS) or through vertical transmission during pregnancy (Congenital Syphilis—CS) (1–3). Although curable and preventable through barrier methods (such as condoms), syphilis has been neglected and still represents a global public health concern due to inadequate diagnosis and treatment, resulting in morbidity and mortality in newborns and untreated infected people (4, 5).

According to the World Health Organization (WHO), over 357 million new cases of curable sexually transmitted infections (STIs) were diagnosed among young adults (15–49 years) in 2016 alone, of which 6 million were associated with syphilis (6). Currently, Brazil, Europe Union, and the USA are facing a silent syphilis epidemic that affects millions of patients annually (7–10). The 2022 Epidemiological Bulletin of Syphilis reported that, between January 1 and June 30, 2021, 167,523 new cases of AS were identified, followed by 74,095 cases of syphilis in pregnancy (SIP) and 27,019 cases of CS (11). In Brazil, there was on average one new case of AS every 1 min and 40 s, 1 new case of SIP every 4 min and 15 s, and 1 new case of CS every 11 min (12, 13).

Syphilis is diagnosed through serological tests, such as the Venereal Disease Research Laboratory (VDRL), a non-treponemal test. If a non-treponemal test is reactive, a treponemal test, e.g., T. Pallidum hemagglutination assay (TPHA), is performed to confirm the diagnosis. However, serological tests have limitations, such as the time of infection, which may present false-negative results in cases of early or late infections (14).

Adequately treated patients are expected to show significantly reducing non-treponemal antibody titers, but there are cases where titers persist for months to years and may represent a false-positive result when retested (15, 16). Furthermore, it is also possible to observe false-positive results in patients with an autoimmune condition, such as systemic lupus erythematosus, with other infectious diseases, such as brucellosis, or even in pregnancy (15). As syphilis shares several clinical manifestations and clinical characteristics with other treponemal and non-treponemal diseases, a safe clinical diagnosis is necessary, always performed by well-prepared and highly accurate laboratory tests (17).

In parallel, computational methods have been applied in health to aid diagnosis and treatment decisions, including in the diagnosis of STIs, recommendation of adequate treatment, and predictions on the probability of infection (18–21). Predictive analytics is a method for predicting future risks based on current and prior data, assisted often by data mining, machine learning, and novel statistical techniques (22). These techniques are used to develop an inference mechanism, a set of rules that can be applied to a dataset to render a mathematical function that can predict or infer knowledge about that data (19).

Artificial intelligence (AI) has been used to determine characteristics of individuals who are more prone to STIs, such as men who have sex with men (MSM), transgender people, sex workers, those who use stimulants to enhance and prolong sexual experiences (known as chemsex practitioners), and pre-exposure prophylaxis users (PrEP) who do not use condoms (23). For AI systems to be deployed, they need to be trained using data generated from clinical interactions. These data can be collected during clinical activities such as screening, diagnosis, and treatment of patients so that the AI systems can learn the similarities between groups and associations between the characteristics of subjects. This data can also include demographic data, clinical notes of health professionals, electronic records from medical devices, data from physical exams, and laboratory and imaging results. AI includes, among others, machine learning (ML) techniques that analyze structured data, such as images and genetic data, and natural language processing (NLP) that can use and integrate data in various forms, such as text, waveform, and images (24).

Basic ML algorithms can be categorized as supervised and unsupervised. Supervised ML methods work by gathering many training cases, which contain labeled inputs and the desired outputs (25). By analyzing the patterns in all the labeled input-output pairs for new cases, the algorithm learns how to produce the correct output for a given input (26, 27). Unsupervised learning infers the underlying patterns by applying similarity measures to unlabeled data to find subclusters of the original data, identify outliers, or produce low-dimensional representations of the data (24).

Against this background, this systematic literature review (SLR) aims to analyze published studies that use computational methods with the application of AI, ML, or other statistical methods to predict the occurrence of syphilis in critical populations and also identify potential gaps and opportunities for future research on different areas for programmatic response to syphilis, such as management of surveillance and comprehensive care.

2. Materials and methods

This research was developed based on the systematic review guidelines proposed by Kitchenham (28) and the PRISMA checklist (29). Initially, as a fundamental part of the protocol, 3 Research Questions (RQ) were formulated (Table 1).

TABLE 1

Table 1. Research questions.

The process of identifying primary studies related to the research object of this SLR consisted of searches in six repositories: Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed. Searches in all databases were performed on August 9, 2022. The following search string (SS01) was used in searches:

• (syphilis) AND (“machine learning” OR “artificial intelligence” OR “computational intelligence” OR “deep learning” OR fuzzy OR “artificial neural network” OR “specialist systems” OR “smart system”).

After identifying and defining the initial set of records, screening was performed to select a subset of eligible primary studies. This process was organized and executed based on the application of three basic procedures: (i) Inclusion Criteria—IC; (ii) Exclusion Criteria—EC; and (iii) Quality Assessment Criteria—QA.

In the first procedure (i), a subset of primary studies was defined from the IC and applied through the filters available in the repositories. In the subsequent step (ii), a screening guided by the EC based on reading the title, abstract, and keywords was performed on the subset of primary studies. Rayyan (30), a web application for systematic reviews, helped carry out step (ii). The search used two inclusion and three exclusion criteria, as shown in Table 2.

TABLE 2

Table 2. Inclusion and exclusion criteria.

To determine the final set of eligible studies to seek answers to the RQ (Table 1), a screening guided by the QA criteria was carried out from the complete reading of the primary articles (Table 3). An evaluation metric called score was used to qualify and classify the studies (as presented in Equation 1). The score is the arithmetic mean of the weights (w) assigned to each QA criterion. The weight (w), which can vary between 0, 0.5, and 1.0, measures how satisfactory the response of that article is to a specific QA criterion, as shown in Equation (2). The preliminary reports that obtained a score ≥ 0.5 (i.e., 0.5 ≤ score ≤ 1.0) were considered eligible for this SLR.

$\begin{array}{ll}score=\frac{1}{n_{\text{QA}}}{\displaystyle \sum _{i=1}^{n_{\text{QA}}}}{w}_{{\text{QA}}_i}& (1)\end{array}$

where:

– n_QA: variable used to represent the total of QA criteria;

– w_QA: variable used to determine the value referring to the weight w attributed to the QA criterion under analysis (see the possible values in Equation 2).

$\begin{array}{ll}{w}_{\text{QA}}=\{\begin{array}{ll}1.0,\hfill & \text{yes},\text{fully describes},\hfill \\ 0.5,\hfill & \text{yes},\text{partially describes},\hfill \\ 0,\hfill & \text{does not describe}.\hfill \end{array}& (2)\end{array}$

TABLE 3

Table 3. Quality assessment.

The scores were assigned by two independent reviewers and elementary data of the final set of eligible studies, extracted based on the RQ, were summarized in Table 4. Studies were included via another method, based on a simple and active search in Science Direct, Springer, and PubMed (Figure 1). This search used the following descriptors: syphilis AND model AND diagnosis.

TABLE 4

Table 4. Set of selected primary studies and their main characteristics.

FIGURE 1

Figure 1. Adapted from PRISMA 2020 flow diagram from the result of the execution of the systematic review protocol.

3. Results

The quantitative results of the execution of the SLR protocol are presented in Figure 1. After identification and screening, 26 primary studies were selected as eligible and included in this SLR to respond to the RQ (Table 1). Relevant data were extracted from the eligible studies and described in Table 4.

3.1. Research question 01

Different computational approaches applied to syphilis and other STIs were identified in the primary studies. It was observed that mostly and regardless of the context and purpose of the application, primary studies explored different computational models of supervised ML–that is, algorithmic models based on previously labeled data to perform classification or regression tasks.

Data-based computational applications for classification or regression tasks generally involve well-organized and pervasive processes that form the following workflow (57): (i) data acquisition, which will serve as input for computational models after the second stage; (ii) data processing, which prepares the data through denoising, feature extraction, feature selection, and data balancing; (iii) training, testing, and selection of the best computational model for application. In the set of primary studies, a more significant effort was evident in processes (ii) and, mainly, (iii).

With data from electronic records from health centers, and especially considering stage (iii), Xu et al. (31) and Elder et al. (48) proposed the most significant number of computational models applied to the context of syphilis. They used different predictive methods: symbolic; probabilistic; distance-based; margin maximization; connectionists; and ensemble learning. Both articles proposed, respectively, 17 and 16 ML models based on regression algorithms (linear and non-linear), Support Vector Machine (SVM), Bagging Ensemble, Boosting Ensemble, Stacking Ensemble, Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Neural Net, and multi-layer perceptron (MLP). As a result, the Boosted Generalized Linear Model (AUC = 0.76) (31) and the Super Learning (cross-validated AUC = 0.76) (48) obtained the best performances.

As for predictive models of ML based on regression and regression for classification, which are widely used in primary studies, the following algorithms were observed: Multivariable/Multivariate Logistic Regression (31, 49, 52, 53); Multinomial Logistic Regression (54); Elastic-Net Regression (31); Logistic Regression (32, 34) Segmented Linear Regression (56); Bayesian Additive Regression Trees (BART) (48); Least Absolute Shrinkage and Selection Operator (LASSO) (35, 48); RIDGE Regression (48); Poisson Regression (36); Generalized Linear Model Logistic Regression (GLM) (48); Boosted GLM (31); Linear Mixed-effects Model (LMM) (35); Linear Regression (32), and Polynomial Regression (32).

Other computational approaches include models based on Deep Learning (43, 49, 55), Convolutional Neural Network Ensemble (51), Decision Tree (47), RF (31, 49, 50), Gradient Boosting Machine (49), Extreme Gradient Boosting (XGBoost) (47–49), Autoregressive Integrated Moving Average (ARIMA) (33, 38, 41), ARIMA with Explanatory Variable (38), Decomposition (38), Generalized Estimating Equations (36), NLP (37, 45), Ontology (42), Complex Networks (46), Knowledge Embedding Representation (40), Sexual Infections as Large-Scale Agent-based Simulation model (44), and Gray Model (39). Table 4 shows the techniques that obtained the best performances in each study and their respective values according to the metric used for evaluation.

3.2. Research question 02

The primary included studies show and explore various applications of computational methods in the context of the syphilis. Two large groups of applications stood out: first, in the classification and identification syphilis indicators (47–55); second, in the prediction of STI-related risks, including syphilis (31–36). Both groups employed trained computational models that have learned patterns from a previously known syphilis-related dataset. Such models were able to use those patterns to make predictions or classify new patient data for establishing syphilis diagnosis.

Other scholars, such as Macedo et al. (37), have explored alternative applications and proposed a health surveillance software architecture modeled with ML algorithms and NLP techniques. These techniques can provide preventive recommendations based on specific terms associated with the disease and published scientific articles. Ruan et al. (45), also using NLP, developed a method to estimate health-adjusted life expectancy in China. Zhang et al. (38), Joshi et al. (41), and Scholz et al. (44) developed applications to estimate syphilis incidence in China, estimate syphilis cases in New York State, and simulate a spread of syphilis in the population of Germany, respectively.

Further, by expanding the possibilities of applications based on computational methods in the syphilis context, the studies also presented models built to analyze networks or social media. The goal aimed to interpret and elucidate the relationships of individuals who post about STIs (42) and to forecast outbreaks based on publications and situational awareness by analyzing scientific articles (43). Tissot et al. (40) presented a model for risk assessment of miscarriage during the early stages of pregnancy. In the same perspective of preventive care, Ou et al. (46) proposed an application to help health agents in the STI screening process.

Two studies explored applications for impact analysis. First, Yan et al. (39) used a computational model developed to analyze the impact of the COVID-19 pandemic on the epidemiological changes of STIs in China. In another approach, Pinto et al. (56) evaluated, through an algorithmic model, the effectiveness of public policy actions in Brazil to reduce AS, SIP, and CS rates.

3.3. Research question 03

Considering the perspective of the large area of health sciences, the applications proposed in the included studies focus on seeking and investigating computational solutions within the scope of three subareas (Figure 2). (1) surveillance accounted for 16 studies (61.54%) (31–46); (2) diagnosis for nine related studies (34.62%) (47–55); and (3) evaluation of health policies for only one (3.85%) (56).

FIGURE 2

Figure 2. Summary of occurrence of articles by application area.

For (1) surveillance, studies (31–46) point to promising technologies or computational methods (Sections 3.1 and 3.2) that act as instruments to subsidize and provide technical support to actions mainly related to the epidemiological surveillance of syphilis and other STIs. Not diverging from the purpose of the different subareas, subarea (2) stood out, where studies focus on seeking innovative and scalable solutions for diagnosing syphilis and other STIs (47–55). Regarding (3) evaluation of health policies, Pinto et al. (56) present a computational solution to investigate and statistically measure the effectiveness of strategies and public policy actions which, from the perspective of public health management, is a vital resource to assist and guide decision-making.

4. Discussion

Although completely curable, syphilis is a sexually transmitted infection caused by T. Pallidum, which is responsible for a silent epidemic wave worldwide (3, 7–9, 58, 59). Even though it is relatively easy to diagnose syphilis through routine laboratory methods, the tests available around the world still present problems, mainly because the most qualified tests are difficult to access, especially in poorer countries. Therefore, the application of computational methods can contribute to the development of new, more accessible (point of care), cheaper, and more accurate tests for the diagnosis of syphilis (60).

Previous studies analyzed the sensitivity and specificity of Syphilis Health Check, a rapid qualitative test to detect human antibodies to T. Pallidum (61), or explored the prevalence of syphilis in men who have sex with men (MSM), identifying critical geographic mapping, trends, and data gaps in Latin America and the Caribbean (62). However, in the current paper, we present a systematic review that investigates the application of computational methods as technological tools to support and induce strategies in the context of syphilis. The analysis revealed a diverse set of studies that used computational methods for epidemiological surveillance of syphilis, diagnosis of syphilis, and assessing the impact of public policies.

In this sense, our review shows the utility of computational tools in furthering the general understanding of syphilis which is worsening a global problem, to guide policy and practice to target better public health interventions such as surveillance and prevention, health care service delivery, and the optimal use of diagnostic tools. For instance, Joshi et al. (41) utilized an ARIMA model to investigate the impact of the COVID-19 pandemic on the diagnosis and reporting of STIs, aiming to inform sexual health program planning. The study analyzed New York State STI surveillance data from January 2015 to December 2019 and found that stay-at-home orders contributed to a decline in sexual activity with casual partners, and adversely affected sexual health services, including a reduction in access to diagnostic testing for STIs.

Zhang et al. (38) showed that disease surveillance data could be used to understand syphilis behavior over time using a time-series models. The study revealed a long-term seasonal and increasing trend for the infection, with secondary syphilis showing more significant seasonal fluctuation than other types of the disease. They concluded that patient's likelihood of seeking treatment for secondary syphilis, which is more severe than the other types, was one reason to explain the observed seasonality. Using logistic regression models, Cuffe et al. (34) revealed that several risk factors were associated with a CS case. This finding may potentially support epidemiological surveillance and healthcare services in directing prevention efforts for CS.

Bao et al. (49) demonstrated that it is possible to use ML techniques to predict syphilis infection using datasets that should be available in most settings, such as STIs symptoms, previous syphilis infection, length of residence in the current place, frequency of condom use with casual male sex partners during receptive anal sex, and the number of sex partners.

Dexter et al. (50) alerted to the limitations of predictive models, especially regarding the low generalization power using health data. They cautioned on generalizing the model's performance in the test and validation dataset to general population use. Understanding the descriptors and how to render the model with high generalizability in the test and validation datasets allows the development of reliable models that reach a favorable result within the scope for which it was intended. Algorithmic bias is an important consideration when applying algorithms generated using learning sets and restricted data, as they can further reinforce and augment prevailing inequalities in health systems (63).

There is a need for establishing population-level integrated data sets that are representative, inclusive, and incorporate public health and surveillance data with health service delivery and socio-economic data to improve the utility of AI and ML techniques to strengthen health systems in general and to improve control of syphilis (64, 65). For this disease, there is encouraging development of technological platforms aimed to minimize errors generated by the fragmentation of data used to survey, diagnose and treat syphilis. For example, in Brazil, the Salus Platform Integrates surveillance data with primary health care data and applies ML to improve work processes and response in health crisis scenarios (66, 67). This Platform has also integrated a model of Research on Knowledge, Attitudes, and Practices in the Population into its technological architecture, adapted from the national survey carried out by the Ministry of Health, the Search of Knowledge, Attitudes, and Practices in the Brazilian population (PCAP) (68). With this, it is possible to investigate patient's knowledge, attitudes, and practices related to syphilis, HIV, and other STIs infection.

There are great possibilities with ML to improve and better target surveillance and testing for syphilis and to help inform the development of more efficient and timely diagnostic processes for syphilis and in health surveillance. These developments can help benefit the fight against syphilis, but also other infectious diseases by paving the way for the development of rapid incidence assays to characterize emerging and worsening epidemics (69).

In the context of Brazil, which has the Brazilian National Health System (SUS), with a tripartite governance framework underpinned by a regionalized and hierarchical network of healthcare providers organized according to the complexity of care, behavioral surveys can be carried out when patients seek health care (70, 71). However, for this to happen in SUS, health policies, public health, surveillance, and healthcare service delivery activities need to operate more effectively in an integrated manner (72). Brazil's suboptimal response to COVID-19 has shown the need for better coordination of health policies, public and healthcare delivery, and integrated datasets that can be harnessed for the application of ML methods (73–75).

Results of this study show that analysis using computational techniques could help inform public health and healthcare delivery responses to the worsening syphilis epidemic around the world. But for this to happen, surveillance and policies developed to inform public health and healthcare delivery interventions must be better coordinated. The fact is that health sciences have advanced a lot, particularly with digital health, surpassing the analog world. Therefore, we come from a place where health was more restricted in terms of access to care, as diagnosis methods were only carried out using expensive and difficult-to-access equipment that required super specialists to operate and issue medical reports.

Surveillance actions for STIs such as syphilis, coupled with novel AI-based technologies and tools, contribute toward overcoming the delays in reports drawn on case notification and the shortcomings in current STI data collection. Optimal STI surveillance is contingent on timely and accurate data, yet surveillance data are generally delayed or unavailable (76). In Brazil, which has experienced a syphilis epidemic since 2016 (77), epidemiological reports on syphilis have usually been released belatedly, usually by more than a year. Thus, in this case, how to make decisions that rely only on delayed data that reflects previous scenarios? (78).

Against this background, IA may enhance surveillance, serving as a tool to support decisions about public health interventions in the context of STIs. Therefore, this could provide part of the answer to this public health problem. According to Young et al. (76), available research on STIs has shown that AI can predict syphilis rates at the small-town level by parsing publicly available social media data regarding people's sexual attitudes and behaviors associated with syphilis. This method, known as Rumor analysis, is highly cumbersome through traditional surveillance methods. However, this is not the case when AI-based tools are used because they allow the same analyses to be performed within seconds (79).

Today we are living the transition from this analogical world of health to a fully digital world; the world is experiencing an important process of digital transformation in health. However, for this movement in digital health to be successful and achieve better social results, science must also look at neglected diseases such as syphilis. Advances in health with AI cannot only be used to increase the profits of the health industry; they must also target social inequities and injustices and develop new diagnostic methods to increase access to health for all who need it. This is the way of the future. Using digital health, based on computational methods such as AI, and all its potential to create new diagnostic methods, new tests, and new forms of prevention against STIs, for example, would be a great advance.

Cheaper technologies at the point-of-care that can be operated at distances—telemedicine and telediagnosis—will certainly contribute to reducing inequalities health access, an important contribution to global health (60). Syphilis is a secular disease, but there are indications it is an ancient ailment, rendering senseless the fact it is still a neglected disease by global science. It is necessary to move forward in the present—right now—so that in the future there will be no more children dying from congenital syphilis. This is a very noble goal for science, for health, for digital health and for those who study the application of AI in health.

5. Conclusions

This article investigates the literature, based on a systematic review protocol, to identify and highlight studies exploring applications based on computational methods or approaches in the context of syphilis. The execution of the SLR protocol yielded 26 primary studies, which were considered eligible. Our findings reveal a substantial diversification of algorithmic models, regardless of the purpose of application, and three subareas of concentration in the field of health sciences: (1) surveillance (16 studies—61.54%) (31–46); (2) diagnosis (nine studies—34.62%) (47–55); and (3) evaluation of health policies (one study—3.85%) (56).

By showing computational models capable of being tools to support STI control and surveillance actions, the studies show promising outcomes. The use of several ML models in the context of syphilis, for example, exhibit a tendency toward consolidation of algorithms for classification and regression tasks. However, there are still ambitious challenges to be explored, such as evaluating the generalization capacity of models considering different global populations, identifying biases in data, and investigating universal access to applications.

A limitation of this review was the impossibility of defining the best predictors for the analysis of syphilis due to the diversity of methods, datasets, and variables used. In addition, the review findings could not establish a technique with good generalizability for the implemented models.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

GA, FF, and RV contributed to conception and design of the study. GA and FF did collection, organizing, and review of the literature. GA, DB, and FF wrote the first draft of the manuscript. GA, FF, DB, IB, LG-L, AS-M, TP, and RV wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

The Norte-Grandense Foundation for Research and Culture and the Federal University of Rio Grande do Norte were responsible for financing the development of this work through the Decentralized Section Term (TED 54/2017), signed by the Federal University of Rio Grande do Norte and the Ministry of Health of Brazil.

Acknowledgments

We kindly thank the Laboratory of Technological Innovation in Health (LAIS) of the Federal University of Rio Grande do Norte (UFRN) and Ministry of Health Brazil for supporting the research.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Kojima N, Klausner JD. An update on the global epidemiology of syphilis. Curr Epidemiol Rep. (2018) 5:24–38. doi: 10.1007/s40471-018-0138-z

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Peeling RW, Mabey D, Kamb ML, Chen XS, Radolf JD, Benzaken AS. Syphilis. Nat Rev Dis Prim. (2017) 3:17073. doi: 10.1038/nrdp.2017.73

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Gilmour LS, Walls T. Congenital syphilis: a review of global epidemiology. Clin Microbiol Rev. (2023) 15:e00126–22. doi: 10.1128/cmr.00126-22

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Brasil. Manual técnico para o diagnóstico da sífilis [recurso eletrônico]. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Doenças de Condiç˜es Cr^nicas e Infecç˜es Sexualmente Transmissíveis (2021). Available online at: https://www.gov.br/saude/pt-br/assuntos/saude-de-a-a-z/s/sifilis/arquivos/manual-tecnico-para-o-diagnostico-da-sifilis.pdf (accessed November 16, 2022).

Google Scholar

5. Cooper JM, Sánchez PJ. Congenital syphilis. Semin Perinatol. (2018) 42:176–84. doi: 10.1007/978-3-319-90038-4_19

CrossRef Full Text | Google Scholar

6. World Health Organization. Global Health Sector Strategy on Sexually Transmitted Infections, 2016-2021: Towards Ending STIs. WHO Reference Number: WHO/RHR/16.09 (2016). Available online at: https://www.who.int/publications/i/item/WHO-RHR-16.09 (accessed November 14, 2022).

Google Scholar

7. Lynn W, Lightman S. Syphilis and HIV: a dangerous combination. Lancet Infect Dis. (2004) 4:456–66. doi: 10.1016/S1473-3099(04)01061-8

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Kenyon CR, Osbak K, Tsoumanis A. The global epidemiology of syphilis in the past century – a systematic review based on antenatal syphilis prevalence. PLoS Negl Trop Dis. (2016) 10:1–23. doi: 10.1371/journal.pntd.0004711

PubMed Abstract | CrossRef Full Text | Google Scholar

9. World Health Organization. Report on Global Sexually Transmitted Infection Surveillance (2018). Available online at: https://www.who.int/publications/i/item/9789241565691 (accessed April 1, 2023).

Google Scholar

10. Bezerra M, Fernandes F, de Oliveira Nunes J, de Araújo Baltar S, Randau K. Congenital syphilis as a measure of maternal and child healthcare, Brazil. Emerg Infect Dis. (2019) 25:1469–76. doi: 10.3201/eid2508.180298

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Brasil. Boletim Epidemiológico: Sífilis. Secretaria de Vigilância em Saúde—Ministério da Saúde (2020). Available online at: https://www.gov.br/aids/pt-br/centrais-de-conteudo/boletins-epidemiologicos/2020/sifilis/boletim_sifilis_2020.pdf/view (accessed November 14, 2022).

Google Scholar

12. Marques dos Santos M, Lopes AKB, Roncalli AG, Lima KC. Trends of syphilis in Brazil: a growth portrait of the treponemic epidemic. PLoS ONE. (2020) 15:1–11. doi: 10.1371/journal.pone.0231029

PubMed Abstract | CrossRef Full Text | Google Scholar

13. de Andrade IGM, Valentim RAM, Oliveira CAP. The influence of the No Syphilis Project on congenital syphilis admissions between 2018 and 2019. Braz J Sex Trans Dis. (2020) 32:e203205.

Google Scholar

14. Soreng K, Levy R, Fakile Y. Serologic testing for syphilis: benefits and challenges of a reverse algorithm. Clin Microbiol Newsl. (2014) 36:195–202. doi: 10.1016/j.clinmicnews.2014.12.001

PubMed Abstract | CrossRef Full Text | Google Scholar

15. García-Legaz Martínez M, Hernández-Bel P, Magdaleno-Tapial J, Martínez-Doménech A, Navalpotro D, Alegre-de Miquel V, et al. Usefulness of new automated treponemal tests in the diagnosis of early syphilis: a series of 15 cases. Actas Dermosifiliogr. (2020) 111:135–42. doi: 10.1016/j.adengl.2020.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Forrestel AK, Kovarik CL, Katz KA. Sexually acquired syphilis: Laboratory diagnosis, management, and prevention. J Am Acad Dermatol. (2020) 82:17–28. doi: 10.1016/j.jaad.2019.02.073

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Shah D, Marfatia Y. Serological tests for syphilis. Indian J Sex Transmit Dis AIDS. (2019) 40:186–91. doi: 10.4103/ijstd.IJSTD_86_19

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Sowumni OY, Misra S, Fernandez-Sanz L, Medina-Merodio JA. Framework for academic advice through mobile applications. In:Gołuchowski J, Pańkowska M, Barry C, Lang M, Linger H, Schneider C, , editors. Information Systems Development: Complexity in Information Systems Development (ISD2016 Proceedings). (ISD2016 Proceedings). Katowice: University of Economics in Katowice (2016). p. 332–44. Available online at: https://aisel.aisnet.org/isd2014/proceedings2016/CogScience/6/ (accessed November 14, 2022).

Google Scholar

19. Abu-Naser SS, Al-Hanjori MM. An expert system for men genital problems diagnosis and treatment. Int J Med Res. (2016) 1:83–6.

Google Scholar

20. Robles-Bykbaev VE, Lóez-Nores M, Pazos-Arias JJ, Arévalo-Lucero D. SPELTA: an expert system to generate therapy plans for speech and language disorders. Exp Syst Appl. (2015) 42:7641–51. doi: 10.1016/j.eswa.2015.06.011

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Thompson T, Sowunmi O, Misra S, Fernandez-Sanz L, Crawford B, Soto R. An expert system for the diagnosis of sexually transmitted diseases – ESSTD. J Intell Fuzzy Syst. (2017) 33:2007–17. doi: 10.3233/JIFS-161242

CrossRef Full Text | Google Scholar

22. Induja SN, Raji CG. Computational methods for predicting chronic disease in healthcare communities. In: 2019 International Conference on Data Science and Communication (IconDSC). Bangalore. (2019). p. 1–6.

Google Scholar

23. Glynn RW, Byrne N, O'Dea S, Shanley A, Codd M, Keenan E, et al. Chemsex, risk behaviours and sexually transmitted infections among men who have sex with men in Dublin, Ireland. Int J Drug Policy. (2018) 52:9–15. doi: 10.1016/j.drugpo.2017.10.008

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. (2017) 2:230–43. doi: 10.1136/svn-2017-000101

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Kotsiantis SB. Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in EHealth, HCI, Information Retrieval and Pervasive Technologies. Amsterdam: IOS Press (2007). p. 3–24.

PubMed Abstract | Google Scholar

26. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. (2018) 2:719–31. doi: 10.1038/s41551-018-0305-z

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Ghosh P, Azam S, Jonkman M, Karim A, Shamrat FMJM, Ignatious E, et al. Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access. (2021) 9:19304–26. doi: 10.1109/ACCESS.2021.3053759

CrossRef Full Text | Google Scholar

28. Kitchenham B. Procedures for Performing Systematic Reviews. Keele: Keele University, Department of Computer Science, Software Engineering Group and Empirical Software Engineering National ICT Australia Ltd. (2004).

Google Scholar

29. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. (2016) 5:210. doi: 10.1186/s13643-016-0384-4

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Xu X, Ge Z, Chow EPF, Yu Z, Lee D, Wu J, et al. A machine-learning-based risk-prediction tool for HIV and sexually transmitted infections acquisition over the next 12 months. J Clin Med. (2022) 11:1818. doi: 10.3390/jcm11071818

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Valentim RAM, Caldeira-Silva GJP, da Silva RD, Albuquerque GA, de Andrade IGM, Sales-Moioli AIL, et al. Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil. BMC Med Inform Decis Mak. (2022) 22:40. doi: 10.1186/s12911-022-01773-1

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Yan X, Wang X, Zhang X, Wang L, Zhang B, Jia Z. The epidemic of sexually transmitted diseases under the influence of COVID-19 in China. Front Public Health. (2021) 9:737817. doi: 10.3389/fpubh.2021.737817

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Cuffe KM, Kang JDY, Dorji T, Bowen VB, Leichliter JS, Torrone E, et al. Identification of US counties at elevated risk for congenital syphilis using predictive modeling and a risk scoring system. Sex Transmit Dis. (2020) 47:290–5. doi: 10.1097/OLQ.0000000000001142

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Young SD, Torrone EA, Urata J, Aral SO. Using search engine data as a tool to predict syphilis. Epidemiology. (2018) 29:574–8. doi: 10.1097/EDE.0000000000000836

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Allan-Blitz LT, Konda KA, Vargas SK, Wang X, Segura ER, Fazio BM, et al. The development of an online risk calculator for the prediction of future syphilis among a high-risk cohort of men who have sex with men and transgender women in Lima, Peru. Sex Health. (2018) 15:261–8. doi: 10.1071/SH17118

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Macedo AA, Pollettini JT, Baranauskas JA, Chaves JCA, A. Health Surveillance Software Framework to deliver information on preventive healthcare strategies. J Biomed Inform. (2016) 62:159–70. doi: 10.1016/j.jbi.2016.06.002

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Zhang X, Zhang T, Pei J, Liu Y, Li X, Medrano-Gracia P. Time series modelling of syphilis incidence in China from 2005 to 2012. PLoS One. (2016) 11:1–18. doi: 10.1371/journal.pone.0149401

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Yan J, Li Y, Zhou P. Impact of COVID-19 pandemic on the epidemiology of STDs in China: based on the GM (1,1) model. BMC Infect Dis. (2022) 22:519. doi: 10.1186/s12879-022-07496-y

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Tissot HC, Pedebos LA. Improving risk assessment of miscarriage during pregnancy with knowledge graph embeddings. J Healthc Informat Res. (2021) 5:359–81. doi: 10.1007/s41666-021-00096-6

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Joshi M, Yuan Y, Miranda W, Chung R, Rajulu DT, Hart-Malloy R. A peek into the future: how a pandemic resulted in the creation of models to predict the impact on sexually transmitted infection(s) in New York State (Excluding New York City). Sex Transmit Dis. (2021) 48:381–4. doi: 10.1097/OLQ.0000000000001377

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Amith M, Fujimoto K, Mauldin R, Tao C. Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health. BMC Med Inform Decis Mak. (2020) 20:269. doi: 10.1186/s12911-020-01287-8

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Serban O, Thapen N, Maginnis B, Hankin C, Foot V. Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification. Inf Process Manag. (2019) 56:1166–84. doi: 10.1016/j.ipm.2018.04.011

CrossRef Full Text | Google Scholar

44. Scholz S, Batram M, Greiner W. The SILAS model: sexual infections as large-scale agent-based simulation. In: Proceedings of the Conference on Summer Computer Simulation. SummerSim '15. San Diego, CA: Society for Computer Simulation International (2015). p. 1–6.

Google Scholar

45. Ruan X, Li Y, Jin X, Deng P, Xu J, Li N, et al. Health-adjusted life expectancy (HALE) in Chongqing, China, 2017: an artificial intelligence and big data method estimating the burden of disease at city level. Lancet Reg Health. (2021) 9:100110. doi: 10.1016/j.lanwpc.2021.100110

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Ou HC, Sinha A, Suen SC, Perrault A, Raval A, Tambe M. Who and when to screen: multi-round active screening for network recurrent infectious diseases under uncertainty. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. AAMAS'20. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems (2020). p. 992–1000.

Google Scholar

47. Wang M, Wei Z, Jia M, Chen L, Ji H. Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records. BMC Med Inform Decis Mak. (2022) 22:41. doi: 10.1186/s12911-022-01776-y

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Elder HR, Gruber S, Willis SJ, Cocoros N, Callahan M, Flagg EW, et al. Can machine learning help identify patients at risk for recurrent sexually transmitted infections? Sex Transmit Dis. (2021) 48:56–62. doi: 10.1097/OLQ.0000000000001264

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Bao Y, Medland NA, Fairley CK, Wu J, Shang X, Chow EPF, et al. Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches. J Infect. (2021) 82:48–59. doi: 10.1016/j.jinf.2020.11.007

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Dexter GP, Grannis SJ, Dixon BE, Kasthurirathne SN. Generalization of machine learning approaches to identify notifiable conditions from a statewide health information exchange. AMIA Joint Summits Transl Sci Proc. (2020) 30:152–61.

PubMed Abstract | Google Scholar

51. Mathur J, Chouhan V, Pangti R, Kumar S, Gupta S. A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19. Dermatol Ther. (2021) 34:e14902. doi: 10.1111/dth.14902

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Lu Y, Ke W, Yang L, Wang Z, Lv P, Gu J, et al. Clinical prediction and diagnosis of neurosyphilis in HIV-negative patients: a case-control study. BMC Infect Dis. (2019) 19:1017. doi: 10.1186/s12879-019-4582-2

PubMed Abstract | CrossRef Full Text | Google Scholar

53. King C, Hughes G, Furegato M, Mohammed H, Were J, Copas A, et al. Predicting STI diagnoses amongst MSM and young people attending sexual health clinics in England: triage algorithm development and validation using routine clinical data. eClinicalMedicine. (2018) 4:43–51. doi: 10.1016/j.eclinm.2018.11.002

PubMed Abstract | CrossRef Full Text | Google Scholar

54. The Standardization of Uveitis Nomenclature (SUN) Working Group. Classification criteria for syphilitic uveitis. Am J Ophthalmol. (2021) 228:182–91. doi: 10.1016/j.ajo.2021.03.039

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Pinoliad SL, Dichoso DAN, Caballero AR, Albina EM. OnyxRay: a mobile-based nail diseases detection using custom vision machine learning. In: Proceedings of the 5th International Conference on Information and Education Innovations. ICIEI '20. New York, NY: Association for Computing Machinery (2020). p. 126–33.

Google Scholar

56. Pinto R Valentim R Fernandes da Silva L Fontoura de Souza G Góis Farias de Moura Santos Lima T Pereira de Oliveira CA . Use of interrupted time series analysis in understanding the course of the congenital syphilis epidemic in Brazil. Lancet Reg Health Am. (2022) 7:100163. doi: 10.1016/j.lana.2021.100163

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Fernandes F, Barbalho I, Barros D, Valentim R, Teixeira C, Henriques J, et al. Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review. Biomed Eng Online. (2021) 20:61. doi: 10.1186/s12938-021-00896-2

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Luo Z, Ding Y, Yuan J, Wu Q, Tian L, Zhang L, et al. Predictors of seronegative conversion after centralized management of syphilis patients in Shenzhen, China. Front Public Health. (2021) 9:755037. doi: 10.3389/fpubh.2021.755037

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Lim J, Yoon SJ, Shin JE, Han JH, Lee SM, Eun HS, et al. Outcomes of infants born to pregnant women with syphilis: a nationwide study in Korea. BMC Pediatr. (2021) 21:47. doi: 10.1186/s12887-021-02502-9

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Barros GMC, Carvalho DDA, Cruz AS, Morais EKL, Sales-Moioli AIL, Pinto TKB, et al. Development of a cyclic voltammetry-based method for the detection of antigens and antibodies as a novel strategy for syphilis diagnosis. Int J Environ Res Public Health. (2022) 19:16206. doi: 10.3390/ijerph192316206

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Bristow CC, Klausner JD, Tran A. Clinical test performance of a rapid point-of-care syphilis treponemal antibody test: a systematic review and meta-analysis. Clin Infect Dis. (2020) 71(Suppl_1):S52–7. doi: 10.1093/cid/ciaa350

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Zoni AC. González MA, Sjögren HW. Syphilis in the most at-risk populations in Latin America and the Caribbean: a systematic review. Int J Infect Dis. (2013) 17:e84–92. doi: 10.1016/j.ijid.2012.07.021

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. (2019) 9:20210090748. doi: 10.7189/jogh.09.020318

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Turbé V, Herbst C, Mngomezulu T, Meshkinfamfard S, Dlamini N, Mhlongo T, et al. Deep learning of HIV field-based rapid tests. Nat Med. (2021) 27:1165–70. doi: 10.1038/s41591-021-01384-9

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Marcus JL, Hurley LB, Krakower DS, Alexeeff S, Silverberg MJ, Volk JE. Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. Lancet HIV. (2019) 6:e688–95. doi: 10.1016/S2352-3018(19)30137-7

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Valentim RAM, Lima TS, Cortez LR, Barros DMS, Silva RD, Paiva JC, et al. The relevance a technology ecosystem in the Brazilian National Health Service's Covid-19 response: the case of Rio Grande do Norte, Brazil. Cien Saude Colet. (2021) 26:2035–52. doi: 10.1590/1413-81232021266.44122020

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Brito T, Lima T, Cunha-Oliveira A, Noronha A, Brito C, Farias F, et al. Salus platform: a digital health solution tool for managing syphilis cases in Brazil-a comparative analysis. Int J Environ Res Public Health. (2023) 20:5258. doi: 10.3390/ijerph20075258

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Brasil. Pesquisa de conhecimentos, atitudes e práticas da população brasileira 2013: Série g. estatística e informação em saúde [recurso eletrônico]. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de DST, Aids e Hepatites Virais. (2016). Available online at: http://antigo.aids.gov.br/pt-br/pub/2016/pesquisa-de-conhecimentos-atitudes-e-praticas-na-populacao-brasileira-pcap-2013 (accessed March 15, 2023).

Google Scholar

69. Incidence Assay Critical Path Working Group. More and better information to tackle HIV epidemics: towards improved HIV incidence assays. PLoS Med. (2011) 8:1–6. doi: 10.1371/journal.pmed.1001045

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Massuda A, Hone T, Leles FAG, de Castro MC, Atun R. The Brazilian health system at crossroads: progress, crisis and resilience. BMJ Global Health. (2018) 3:e000829. doi: 10.1136/bmjgh-2018-000829

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Castro MC, Massuda A, Almeida G, Menezes-Filho NA, Andrade MV, de Souza Noronha KVM, et al. Brazil's unified health system: the first 30 years and prospects for the future. Lancet. (2019) 394:345–56. doi: 10.1016/S0140-6736(19)31243-7

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Almeida AS, Andrade J, Fermiano R, Jamas MT, Carvalhaes MABL, Parada CMGL. Syphilis in pregnancy, factors associated with congenital syphilis and newborn conditions at birth. Texto Contexto Enfermagem. (2021) 30:e20200423. doi: 10.1590/1980-265x-tce-2020-0423

CrossRef Full Text | Google Scholar

73. Knaul FM, Touchton M, Arreola-Ornelas H, Atun R, Anyosa RJC, Frenk J, et al. Punt politics as failure of health system stewardship: evidence from the COVID-19 pandemic response in Brazil and Mexico. Lancet Reg Health Am. (2021) 4:100086. doi: 10.1016/j.lana.2021.100086

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Bigoni A, Malik AM, Tasca R, Carrera MBM, Schiesari LMC, Gambardella DD, et al. Brazil's health system functionality amidst of the COVID-19 pandemic: an analysis of resilience. Lancet Reg Health Am. (2022) 10:100222. doi: 10.1016/j.lana.2022.100222

PubMed Abstract | CrossRef Full Text | Google Scholar

75. da Rocha MA, dos Santos MM, Fontes RS, de Melo ASP, Cunha-Oliveira A, Miranda AE, et al. The text mining technique applied to the analysis of health interventions to combat congenital syphilis in Brazil: the case of the “syphilis no!” project. Front Public Health. (2022) 10:855680. doi: 10.3389/fpubh.2022.855680

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Young SD, Crowley JS, Vermund SH. Artificial intelligence and sexual health in the USA. Lancet Dig Health. (2021) 3:e467–8. doi: 10.1016/S2589-7500(21)00117-5

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Lannoy LH, Santos PC, Coelho R, Dias-Santos AS, Valentim R, Pereira GM, et al. Gestational and congenital syphilis across the international border in Brazil. PLoS ONE. (2022) 17:1–14. doi: 10.1371/journal.pone.0275253

PubMed Abstract | CrossRef Full Text | Google Scholar

78. de Brito Pinto TK, da Cunha-Oliveira ACGDP, Sales-Moioli AIL, Dantas JF, da Costa RMM, Silva Moura JP, et al. Clinical protocols and treatment guidelines for the management of maternal and congenital syphilis in Brazil and Portugal: analysis and comparisons: a narrative review. Int J Environ Res Public Health. (2022) 19:10513. doi: 10.3390/ijerph191710513

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: a systematic review. J Biomed Inform. (2020) 108:103500. doi: 10.1016/j.jbi.2020.103500

PubMed Abstract | CrossRef Full Text | Google Scholar