Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study - Université Toulouse III - Paul Sabatier - Toulouse INP Accéder directement au contenu
Article Dans Une Revue Journal of Clinical Medicine Année : 2022

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study

Anissa Zitouni
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Résumé

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.
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hal-04574671 , version 1 (14-05-2024)

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Antoine Dubuc, Anissa Zitouni, Charlotte Thomas, Philippe Kémoun, Sarah Cousty, et al.. Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study. Journal of Clinical Medicine, 2022, 11 (21), pp.6596. ⟨10.3390/jcm11216596⟩. ⟨hal-04574671⟩
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