Om Parkash1
Asra Tus Saleha Siddiqui1
Uswa Jiwani2Fahad Rind3
Zahra Ali Padhani4Arjumand Rizvi2
Zahra Hoodbhoy5
Jai K. Das4,5*- 1Department of Medicine, Aga Khan University, Karachi, Pakistan
- 2Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
- 3Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
- 4Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- 5Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease.
Methods: We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360).
Findings: We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias.
Interpretation: The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis.
Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
Introduction
Gastrointestinal (GI) pathologies contribute to a significant burden of disease worldwide. With 89 million global disability-adjusted life years (DALYs), GI pathologies contributed 3.5% (2,280 million cases) to the total global DALYs in 2019, with a greater prevalence in low and middle-income countries (LMICs) (1). In 2018, there were more than 36⋅8 million ambulatory visits in the United States for GI symptoms and 43⋅4 million had a primary GI diagnosis. Annually, a total of 22.2 million GI endoscopies were performed, with 284,844 new GI cancers diagnosed and 255,407 deaths (2). Other parts of the world, including LMICs also have an increasing burden of GI pathologies, as 80% of the esophageal cancer burden of the world is from LMICs, with poor survival (3). The morbidity and mortality due to GI causes is higher than other common pathologies and hence underscores a significant burden that GI adds to the overall health care system.
Despite this high burden of disease, there are multiple challenges that hinder the provision of optimal GI care. In LMICs, it is often the lack of resources such as endoscopy equipment and availability of skills and experts for timely diagnosis and intervention (4). In high income regions, these challenges include high costs along with discrepancies in facilities and training (5). Overcoming these challenges would require significant amount of resources and time. Although progress has been made globally to enhance these skills, this capacity is still lagging. However, innovations in technology have proven to be a beacon to overcome these challenges adeptly and efficiently.
The introduction of artificial intelligence (AI) in health care has led to innovations in diagnosis, management and prognosis of many conditions at a fast pace. AI algorithms in gastroenterology have been studied over many years to automate the interpretation of diagnostic procedures in gastroenterology albeit with varying levels of success. Since 2010, AI has explored multiple procedures and pathologies in gastroenterology (6). The AI models have been applied to the interpretation of endoscopy, pill video endoscopy, ultrasound manometry, and microcytoscopy (7–17). Traditionally, these procedures yield large amounts of data which require an expert’s time and attention to draw clinical conclusions. However, AI models in these studies have shown to recognize polyps, areas of inflammation, and degrees of inflammation accurately. A recent randomized controlled trial reported much lower miss rates for a deep learning model for polyp detection compared to the standard (20.1% vs. 31.2%) (18). The massive influx and availability of data, along with promising performance of AI in lesion detection, makes incorporation of AI in healthcare promising. However, there is a need to synthesize the existing literature to quantify the accuracy of AI algorithm in detection of GI disease.
The primary objective of this systematic review and meta-analysis was to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal luminal pathologies, including polyps, neoplasms, inflammatory bowel disease (IBD), celiac disease, and Barrett’s esophagus. The secondary objective was to describe the diagnostic accuracy of different types of AI models for the diagnosis of each GI luminal pathology.
Methods
The protocol for this review was prospectively registered at PROSPERO (CRD42021288360). We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines for diagnostic test accuracy for analysis reporting in this publication (19).
Eligibility criteria and search strategy
We included all observational studies that reported the diagnostic results of an AI algorithm for the detection of GI luminal pathologies when compared to a reference standard (expert opinion or consensus, histopathology, or laboratory testing such as urea breath test for H. pylori etc.). No restrictions were applied based on the age at diagnosis or type of AI algorithm used in the study. Studies with an unclear description of reference standard or type of GI luminal pathologies, published in a language other than English, and those that graded the severity of an already diagnosed disease were excluded. We excluded letters, opinions, preprints, scientific reports, and narrative reviews. Studies based on animals or non-human samples or that presented duplicate data were excluded.
We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify relevant articles published until January 27, 2021. The keywords used for the search included, “Algorithms,” “Artificial Intelligence,” “Machine Learning,” “Deep Learning,” “Supervised Machine Learning,” “Unsupervised Machine Learning,” “Gastroenterology,” “Celiac Disease,” “Inflammatory Bowel Disease*,” “Irritable Bowel Syndrome,” “Polyp*,” “Crohn Disease,” “Gastro*,” “Endoscopy,” “Scopy,” “Capsule Endoscopy,” “Endomicroscopy,” “Colonoscopy,” “Ultrasound Manometry,” “Diagnosis,” “Diagnos*,” “Accuracy,” “Sensitivity and Specificity,” “Area Under Curve,” “Sensitivity,” and “Specificity.” A full search strategy for each database is available in Supplementary material. All records were imported to Covidence, and duplicates were removed.
Screening and data extraction
Two authors (AS and FR) independently screened titles and abstracts to assess for potential eligibility. Full texts of all screened studies were also reviewed by two authors for final selection. We manually searched bibliographies and citations of included studies and relevant systematic reviews to identify any additional relevant articles that might have been missed in the initial search. Eligibility assessment was done by two reviewers at all stages independently, and disagreements were resolved by involving a third reviewer (JKD, OP).
Two authors (AS and UJ) independently extracted information to a pre-formed data extraction sheet on Excel. Data obtained included information about the study (first author, year of publication, journal, study title, country, income region of the country according to the World Bank, aim of the study, study design, study setting, sample size (including size of training and test set), method of population selection, patient characteristics (age range, type of GI luminal pathologies), the AI algorithm used, the reference standard, the type of data used for analysis (per-image, per-lesion, or per-patient analysis), reported performance metrics (sensitivity, specificity, and area under the curve), validation of the model (internal or external) and sub-group data if present.
Risk of bias assessment
The risk of bias was assessed by two authors (AS, UJ) independently using quality assessment for diagnostic accuracy studies-2 (QUADAS-2) tool (20). Domains for risk of bias included patient selection, index test, reference standard, and flow and timing, with the first three domains also considered in terms of applicability concerns. If one of the questions within the domain was scored at high risk of bias, the domain was scored as high risk. Disagreements in data extraction and quality assessment were resolved by discussion with a third reviewer (ZH or JKD).
Data analysis
Where possible, we extracted 2 × 2 contingency tables or data to construct such tables. Contingency tables consisted of true-positive, false-positive, true-negative, and false-negative results, and were used to calculate sensitivity, specificity, and accuracy. External validation is important to establish the quality and generalizability of machine learning models (21), while internally validation alone has a potential to overestimate the accuracy of the model (22), therefore to estimate the accuracy of AI algorithms, we conducted a meta-analysis of studies that provided data for contingency tables separate for externally validated data (test data) and internally validated data (training data). If a study tested more than one AI model or more than one dataset, all contingency tables were included in the meta-analysis.
For all included studies, we entered the data provided into Review Manager (RevMan 5.4.1) software (23) where the sensitivity, specificity and their 95% confidence intervals (CIs) were presented in the form of forest plots and receiver operating characteristic (ROC) curves. This analysis utilized the sensitivity and specificity results from each included study using the metandi command for bivariate model in STATA version 17 (24) to generate hierarchical Summary ROC (HSROC) curves.
Subgroup analyses were conducted based on the type of GI luminal pathologies, AI model, reference standard, and data used for analysis (image, lesions, or patients). Subgroup analyses were performed if at least four studies in each sub-group could be analyzed together. Subgroup analysis was conducted on the various GI luminal pathologies listed, types of AI models used, reference standards and types of input data (per patient, image, or lesion). We grouped AI models together according to their class (Figure 1). We performed separate analysis for externally and internally validated studies. We also conducted an exploratory analysis on internally validated studies to evaluate the diagnostic accuracy of AI models on internally validated data.
Figure 1. Model classification. Source: Brownlee (104).
Results
The search strategy identified 5,586 articles for title/abstract screening, of which 219 full texts were screened for eligibility. Altogether, 73 studies were included in the review. Of these studies, 68 studies were externally validated and five were internally validated. Among the externally validated studies 31 (42.5%) studies were included in the meta-analysis (10, 17, 25–53) while 37 (50.7%) studies were narratively synthesized due to insufficient information to calculate the contingency tables (11–14, 54–86). The five (6.8%) internally validated studies provided sufficient information to calculate contingency tables and were included in the exploratory analysis (87–91). The details of the study flow diagram have been shown in Figure 2.
Figure 2. Study selection.
Among the 73 included studies, 53 (72.6%) were case-control, 17 (23.3%) were cohort, one (1.4%) was a mixed-method study, and two (2.7%) studies failed to define the study design (Tables 1,2). Majority of the included studies were conducted in high income countries (HICs) (n = 54, 73.9%) and upper–middle income countries (UMICs) (n = 14, 19.2%), while four (5.5%) were conducted in both HICs and UMICs, and one (1.4%) in both HIC and LMIC. Diagnostic performance of AI was most tested on colorectal polyps (n = 29, 39.7%), followed by ulcers (n = 7, 9.6%), celiac disease (n = 6, 8.2%), IBD (n = 5, 6.8%), Barrett’s esophagus (n = 3, 4.1%), and gastric polyps (n = 3, 4.1%). Sixteen studies used AI to differentiate between benign and precancerous or cancerous polyps, while 10 studies used AI to diagnose more than one type of disease.
Table 1. Table of included studies (polyps).
Table 2. Table of included studies (other gastrointestinal luminal pathologies).
Only 23 (31.5%) studies reported the age of the participants. Nineteen studies (82.6%) reported on adult population, three (13%) on pediatric population and one (4.3%) study reported on both. At least one dataset used in 16 (21.9%) studies, was a publicly available database (34, 37–39, 42, 43, 47, 50, 54, 58, 60, 67, 68, 71, 76, 81). Sixty three (86.3%) studies used their own data (10–14, 17, 25–36, 38–41, 44–46, 48, 49, 51–53, 55–59, 61–66, 69, 70, 72–91), while 18 (28.6%) collected data prospectively (11, 13, 26, 27, 32, 35, 38, 39, 45, 55, 56, 59, 65, 70, 73, 74, 80, 87).
Methodological quality of the included studies
Details of risk of bias and applicability concerns are presented in Figure 3. Fifty-six studies (76.7%) had a high risk of bias in patient selection (11–13, 17, 25–27, 29–31, 34, 36–39, 41–44, 46, 47, 50–54, 57, 58, 60–72, 74–79, 81, 83–86, 88–91), mostly due to the case-control study design (n = 54, 74%) (11–13, 17, 25–27, 29–31, 34, 36–39, 41–44, 46, 47, 50–54, 57, 58, 60–64, 66–72, 75–79, 81, 83–86, 88–91). Two studies had low risk of bias for patient selection because of random participant sampling, and due to appropriate exclusion of participants (55, 87), while 15 studies (20.5%) did not provide sufficient information for risk classification (10, 14, 28, 32, 33, 35, 40, 45, 48, 49, 56, 59, 73, 80, 82). Majority of the studies had unclear (n = 63, 86.3%) (10, 11, 13, 14, 17, 25–33, 35, 36, 38, 39, 41–45, 48–55, 57–59, 61–88, 91) or high (n = 9, 12.3%) (12, 34, 37, 46, 47, 56, 60, 89, 90) risk of bias for index test, which was most often due to insufficient information on blinding of the index test (n = 67, 91.8%) (11–14, 17, 25–33, 36–39, 41–64, 66–72, 74–91) and pre-specification of diagnostic threshold (n = 61, 83.6%) (10, 11, 13, 14, 17, 25, 26, 29–33, 35, 36, 38, 39, 41–45, 48–55, 57–59, 61–88, 91). Fifty-four (74.0%) studies were classified as low risk for reference standard due to correct classification of the condition and interpretation of diagnostic test without the knowledge of the index test (10–13, 17, 26–36, 38, 39, 41, 44, 45, 47–49, 51–53, 56, 58–63, 69–72, 74–79, 81–90), while 49 (67.1%) were categorized as low risk for flow and timing biases due to inclusion of all the participants, appropriate interval between index test and reference standard and due to provision of same reference standard to the all the ails of risk of bias and applicab participants (10, 11, 13, 17, 26–33, 35–37, 41, 45, 48, 51–53, 55–64, 69, 70, 72, 74–79, 81–85, 87–89, 91). No study had concerns about applicability in all three domains.
Figure 3. Risk of bias in the included studies.
Detection of any gastrointestinal luminal pathology by externally validated artificial intelligence models
Thirty-six studies provided sufficient information to calculate contingency table values (10, 17, 25–53, 87–91). Of these, 31 studies (86.1%) conducted external validation and were included in the meta-analysis (10, 17, 25–53). Contingency tables of per-image analyses were used for the meta-analysis; if the study did not report per-image analyses, then per-lesion results were included. If neither per-image nor per-lesion analyses were reported, per-patient results were used. The hierarchical summary ROC curve of these studies is shown in Figure 4. The overall sensitivity from these studies was 91.9% (95% CI: 89.0–94.1) while the specificity was 91.7% (95% CI: 87.4–94.7).
Figure 4. Hierarchical summary ROC curve for externally validated studies.
Subgroup analyses based on different gastrointestinal luminal pathologies
Polyps (n = 13, 36.1%) (29, 34, 38, 42–48, 50, 52, 91) and neoplasms (n = 14, 38.9%) (25–27, 32, 33, 36, 37, 39, 40, 45, 48, 87, 89, 90) were the most commonly reported GI luminal pathologies. Two studies (5.5%) modeled AI for the detection of celiac disease (17, 49), while ulcers (51), IBD (10), and GERD (88) were reported in one study (2.8%) each. The sensitivity of AI models for detecting polyps was 94.0% (95% CI: 88.7–97.0) and the specificity was 95.4% (89.9–98.0) (Figure 5A). AI models demonstrated a sensitivity of 86.3% (95% CI: 82.0–89.7) and specificity of 82.6% (95% CI: 75.6–87.9) for diagnosing GI luminal neoplasms (Figure 5B). No subgroup analyses were performed for the other pathologies due to insufficient number of studies.
Figure 5. Hierarchical summary ROC curve for detecting GI luminal diseases. (A) GI polyps and (B) GI neoplasms.
Subgroup analyses based on artificial intelligence model
Twenty-two studies used deep learning (61.1%) (17, 25, 26, 28, 30–32, 34, 36–42, 44–46, 51–53, 89) and 15 studies used ensemble methods (41.7%) (10, 27, 29, 33, 42–44, 47–50, 87, 88, 90, 91) for the detection of GI luminal pathologies, while the other AI models reported by the studies included artificial neural networks (ANN) (n = 1, 2.8%) (35), k-nearest neighbors (kNN) (n = 1, 2.8%) (50) learning vector quantization (LVQ) (n = 1, 2.8%) (44) and decision trees (n = 1, 2.8%) (50). The sensitivity of deep learning models to detect any GI luminal pathology was 89.8% (95% CI: 85.9–92.7) while that of ensemble methods was 95.4% (95% CI: 91.3–97.6). The specificity of deep learning and ensemble methods was 91.9% (95% CI: 85.7–95.6) and 90.9% (95% CI: 86.2–94.1), respectively (Supplementary Figures). ANN, k-NN, LVQ, and decision trees weren’t included in the subgroup analyses due to the lack of sufficient number of studies.
Subgroup analyses based on different reference standards
Twenty (55.5%) studies used expert diagnosis (17, 27, 28, 34, 36–38, 41–47, 50–53, 88, 91) and 14 (38.9%) studies used histopathology as the reference standard (10, 25, 26, 32, 33, 38–40, 45, 48, 49, 87, 89, 90). The sensitivity and specificity of AI models when compared to expert opinion was 90.5% (95% CI: 86.5–93.4) and 93.3% (95% CI: 88.1–96.4). Studies that used histopathology as the reference standard reported 79.8% (95% CI: 38.2–96.2) sensitivity and 97.6% specificity (95% CI: 94.0–99.1) (Supplementary Figures).
Detection of any gastrointestinal luminal pathology by internally validated artificial intelligence models
The performance of AI models on internally validated data (n = 5, 13.8%) (87–91) was similar to its performance on externally validated data (sensitivity: 91.9%; 95% CI: 89.0–94.1, specificity: 91.7%; 95% CI: 87.4–94.7). The exploratory analysis of internally validated studies showed a sensitivity of 92.9% (95% CI: 89.3–95.4) and specificity of 90.1% (83.8–94.1%).
Discussion
This meta-analysis aimed to synthesize the existing evidence regarding diagnostic accuracy of AI models in detecting common GI luminal pathologies as compared to the reference standard. AI models such as deep learning techniques and ensemble methods were the most commonly deployed models to detect GI luminal pathologies with reported high sensitivity and specificity (> 90%). The most common GI luminal pathologies investigated were the occurrence of polyps and GI neoplasm, both of which had high accuracy.
AI is rapidly gaining momentum across various industries including healthcare. AI has been used in diagnostics, management and improving administrative efficiency of healthcare systems (92). Benefits of AI have been reported in several fields of medicine including cardiology (93), radiology (94) and pediatrics (95). Gastroenterologists are a subset of clinicians who handle large volumes of clinical as well as imaging data obtained through various procedures such as endoscopy and colonoscopy (96). AI algorithms have proven to show high accuracy in diagnosing various gastroenterological pathologies such as polyps, Barrett’s esophagus, celiac disease and Inflammatory Bowel disease (96). These algorithms range from neural networks to ensemble methods such as support vector machines and deep learning techniques (96). In the current review, deep learning and ensemble methods were the most commonly used AI models in the included studies. Our study reported a sensitivity of 86.3% and specificity of 82.6% for diagnosing GI luminal neoplasms. This is similar to the meta-analysis done by Zhang et al. which reported a sensitivity of 94% and specificity of 82% for AI models on esophageal neoplasms only (97). Similarly, a meta-analysis of the accuracy of AI was superior to experts in the detection of conditions such as Barrett’s esophagus and helicobacter Pylori infection (98).
From the 73 studies included in this review, only one study was from a LMIC (Pakistan) while the remaining were from UMIC or HICs. As demonstrated by the Global Burden of Disease data, the burden of benign and neoplastic GI pathologies in LMICs is significantly higher than UMIC and HICs (99). The disproportionately high prevalence of disease alongside poor access to healthcare facilities, lack of equipment and trained professionals in these regions contributes to poor health outcomes (100). It is in these regions that AI may play a disruptive role in healthcare. The areas of impact of AI in LMICs have been well documented by the United States Agency for International Development (USAID) where physician decision support systems may be one of the domains where deployment of these algorithms could help increase access and high-quality care for medical conditions such as GI pathologies which are highly prevalent in these regions (101). Despite this potential implication and similar to other complex conditions prevalent in LMICs (102), published literature on use of AI in healthcare in these regions is lacking.
As seen in the current review as well, majority of literature on AI in medicine is retrospective. The number of prospective studies where AI is implemented in a clinical setting is limited. There is lack of methodological rigor in the design and conduct of the studies published with the risk of bias in most of the domains stated as unclear or high. It is for this reason that clinicians specifically and health systems in general, lack the confidence in such algorithms thus precluding implementation and large-scale benefits of AI in real world settings (103). Future work needs to include methodological rigor in prospectively designed collaborative studies to demonstrate the use of AI in detection of GI pathologies in HICs as well as LMICs. With higher accuracy of detecting these conditions as compared to a human interpreter, the implications of AI in terms of efficient use of resources and better patient outcomes can be substantial.
To the best of our knowledge, this is the first meta-analysis on the diagnostic accuracy of AI algorithms in both upper and lower GI pathologies. Compared to other reviews, we also explored various subgroups in terms of best performing AI models and across world regions where these studies had been conducted. However, there are some limitations of our work. Our review included studies published in the English Language only. This review focused on certain GI luminal pathologies and is no means an exhaustive exercise to include other GI conditions such as liver, pancreatic and biliary pathology. We also did not include studies where AI models utilized in gastroenterological radiology were compared to expert radiologists.
AI models have the potential to accurately diagnose GI lesions based on endoscopic findings as compared to experts. These results could have significant implications for patient related outcomes in resource constrained settings where trained personnel to interpret these images are limited. However, to fully reap the benefits of AI models, prospectively designed large, multi-center studies are required to demonstrate the effectiveness of the results and for implementation of AI into routine clinical practice across HIC and LMICs.
Data availability statement
The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
AS, UJ, and FR were involved in the literature search, data collection, analysis, and writing. AR were involved in data analysis, interpretation, and writing. OP, ZH, ZP, and JD were involved in study design, data analysis, interpretation, and writing of the manuscript. All authors contributed to the article and approved the submitted version.
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2022.1018937/full#supplementary-material
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Keywords: artificial intelligence, systematic review, gastroenterology, diagnostic accuracy, pathologies
Citation: Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z and Das JK (2022) Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front. Med. 9:1018937. doi: 10.3389/fmed.2022.1018937
Received: 15 August 2022; Accepted: 03 October 2022;
Published: 04 November 2022.
Edited by:
Adina Turcu-Stiolica, University of Medicine and Pharmacy of Craiova, RomaniaReviewed by:
Mihaela-Simona Subtirelu, University of Medicine and Pharmacy of Craiova, RomaniaNicole Rübsamen, Faculty of Medicine, University of Münster, Germany
Copyright © 2022 Parkash, Siddiqui, Jiwani, Rind, Padhani, Rizvi, Hoodbhoy and Das. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jai K. Das, amFpLmRhc0Bha3UuZWR1