Park, SojeongSaw, Shier NeeLi, XiutingPaknezhad, MahsaCoppola, DavideDinish, U. S.Ebrahim Attia, Amalina BiniteYew, Yik WengGuan Thng, Steven TienLee, Hwee KuanOlivo, MaliniBIOMEDICAL ENGINEERINGDEPARTMENT OF COMPUTER SCIENCE2022-10-122022-10-122021-05-27Park, Sojeong, Saw, Shier Nee, Li, Xiuting, Paknezhad, Mahsa, Coppola, Davide, Dinish, U. S., Ebrahim Attia, Amalina Binite, Yew, Yik Weng, Guan Thng, Steven Tien, Lee, Hwee Kuan, Olivo, Malini (2021-05-27). Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomedical Optics Express 12 (6) : 3671-3683. ScholarBank@NUS Repository. https://doi.org/10.1364/boe.4151052156-7085https://scholarbank.nus.edu.sg/handle/10635/232457Atopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging. We conducted a comprehensive analysis using three machine-learning models, random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) for classifying healthy versus AD conditions, and sub-classifying different AD severities using RSOM images and clinical information. CNN model successfully differentiates healthy from AD patients with 97% accuracy. With limited data, RF achieved 65% accuracy in sub-classifying AD patients into mild versus moderate-severe cases. Identification of disease severities is vital in managing AD treatment. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Article