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Year : 2020  |  Volume : 11  |  Issue : 6  |  Page : 910-914

Assessment of tibot® artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study

1 Department of Dermatology, Dr DY Patil Medical College and Hospital, Nerul, Navi Mumbai, India
2 Department of Pharmacology, Dr DY Patil Medical College and Hospital, Nerul, Navi Mumbai, India
3 CEO, Polyfins Technology Inc, Fremont, CA, USA
4 Clinical Research Nurse, Polyfins Technology Inc, Mumbai, Maharashtra, India

Correspondence Address:
Anant Patil
Department of Pharmacology, Dr. DY Patil Medical College and Hospital, Navi Mumbai - 400 706, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/idoj.IDOJ_61_20

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Objective: To analyze the accuracy of Tibot artificial intelligence (AI) application tool in predicting the diagnosis of dermatological conditions. Material and Methods: In this prospective, observational study photographs of dermatological lesions with other details of patients having different skin conditions were fed in the AI application for the diagnosis. Predictions given by the Tibot AI application were compared with diagnosis done by the dermatologist. The performance of AI application was evaluated using accuracy, precision, and recall. Results: Data of 398 patients were included in the application of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. Conclusion: In the preliminary stages, AI application tool showed promising results in diagnosing skin conditions. The accuracy and predictive value of the test may improve with the expansion of the database.

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