DEEP LEARNING-BASED PHENOTYPING OF FOREFOOT MORPHOLOGY IN HEREDITARY THORACIC AORTIC DISEASES

ABSTRACT Hereditary thoracic aortic diseases (HTAD) are often associated with multifaceted phenotypic manifestations in different anatomical districts, including skeletal abnormalities. Therefore, diagnostic criteria account for multiple parameters to compute a systemic risk score. Despite the forefoot is known to be different in HTAD, its complex morphology is difficult to be quantified objectively and it is not currently considered in diagnostic criteria. Here, we investigated the potential application of artificial intelligence to compute a HTAD risk score from smartphone-acquired images of the forefoot. To this end, we conducted a pilot study including 44 adults, of which 22 had high risk of HTAD (in line with EACTS/STS guidelines 2024 and revised Ghent criteria). The remaining 22 individuals did not show characteristic features indicative of HTAD. A deep learning architecture was then trained to compute a risk score using specific strategies to account for limited sample sizes: transfer learning and leave-one-out cross validation. The computed risk score was significantly higher in the HTAD group with respect to the control group (p < 0.0001), achieving remarkable sensitivity (82%) and specificity (91%), with an AuC of 0.94. Altogether this study highlights the usefulness of AI to assist the analysis of complex morphological traits, potentially enabling a greater number of healthcare professionals to identify patients at risk of HTAD and readily address them to a proper clinical examination. The study was approved by the Swiss Cantonal Ethics Committee with protocol number 2023-00643. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by Istituto Cardiocentro Ticino (SG, SD) and Università della Svizzera italiana with a Core funding and FIR Grant (DUP) Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Swiss Cantonal Ethics Committee with protocol number 2023-00643. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes ↵& co-first authors Data Availability Data are available from the corresponding authors upon reasonable request