Frontotemporal Dementia: A Systematic Review of Machine LearningArtificial Intelligence approaches in Differential Diagnosis

Serena Dattola ¹ Augusto Ielo ^1* Giuseppe Varone ^2,3 Alberto Cacciola ⁴ Angelo Quartarone ¹ Lilla Bonanno ¹

• ¹ IRCCS Centro Neurolesi Bonino-Pulejo, Messina, Italy
• ² Brain Stimulation Mechanisms Laboratory, Division of Depression and Anxiety Disorders, McLean Hospital, 115 Mill St, Belmont MA, 02478, USA, Belmont, United States
• ³ Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, United States
• ⁴ Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive degeneration of the frontal and temporal lobes, leading to significant changes in personality, behavior, and language abilities. Early and accurate differential diagnosis between FTD, its subtypes, and other dementias, such as Alzheimer's disease (AD), is crucial for appropriate treatment planning and patient care. Machine learning (ML) techniques have shown promise in enhancing diagnostic accuracy by identifying complex patterns in clinical and neuroimaging data that are not easily discernible through conventional analysis.This systematic review, following PRISMA guidelines and registered in PROSPERO, aimed to assess the strengths and limitations of current ML models used in differentiating FTD from other dementias neurological disorders. A comprehensive literature search from 2013 to 2024 identified 25 eligible studies involving 6471 6544 patients with dementia, including 3024 2984 with FTD, and 3324 3437 with AD, 103 mild cognitive impairment (MCI) and 20 Parkinson's disease dementia or probable dementia with Lewy bodies (PDD/DLBPD).The review found that Support Vector Machines (SVMs) were the most frequently used ML technique, often applied to neuroimaging and electrophysiological data. Deep learning methods, particularly convolutional neural networks (CNNs), have also been increasingly adopted, demonstrating high accuracy in distinguishing FTD from other dementias. The integration of multimodal data, including neuroimaging, EEG signals, and neuropsychological assessments, has been suggested to enhance diagnostic accuracy. However, limitations such as small sample sizes, class imbalance, and lack of standardization across studies were noted.1 Dattola et al.Although the findings highlight the promising role of ML techniques in advancing the differential diagnosis of FTD. further research with larger, standardized datasets is needed to improve generalizability and clinical applicability.ML techniques showed strong potential for improving FTD diagnosis, but challenges like small sample sizes, class imbalance, and lack of standardization limit generalizability. Future research should prioritize the development of standardized protocols, larger datasets, and explainable AI techniques to facilitate the integration of ML-based tools into real-world clinical practice.