{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T17:28:44Z","timestamp":1786728524648,"version":"build-2736575974"},"reference-count":17,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Oral health is critical to overall well-being, with abnormalities leading to various health issues. Pemphigus is an autoimmune disease characterized by painful blisters on the skin and mucous membranes, necessitating accurate diagnosis and timely treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>This study aims to develop an artificial intelligence (AI) tool utilizing deep learning techniques for the detection of pemphigus from clinical and histological images, enhancing diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A supervised learning approach was employed, utilizing a pre-trained MobileNet deep learning model to classify histology images into healthy and pemphigus categories. The dataset was prepared through cropping, resizing, and labeling of images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The MobileNet model achieved an accuracy of over 92% in distinguishing between normal mucosa and pemphigus lesions. The proposed AI tool can serve as an initial diagnostic check, facilitating faster treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The implementation of an AI-based diagnostic tool for pemphigus detection demonstrates significant potential in reducing the diagnostic burden in clinical settings. Future work will focus on real-time disease detection applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijd.ijd_341_25","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T07:01:57Z","timestamp":1769065317000},"page":"199-203","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases"],"prefix":"10.4103","volume":"71","author":[{"given":"Pravallika","family":"Kakada","sequence":"first","affiliation":[{"name":"From the Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratibha","family":"Ramani","sequence":"additional","affiliation":[{"name":"From the Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkatesan","family":"Rajinikanth","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. R.","family":"Janaki","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Madras Medical College, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"R1-20260501","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1097\/DAD.0000000000001838","article-title":"Histopathology of pemphigus vulgaris revisited","volume":"43","author":"Manocha","year":"2021","journal-title":"Am J Dermatopathol"},{"key":"R2-20260501","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1067\/mjd.2000.107488","article-title":"Sensitivity and specificity of clinical, histologic, and immunologic features in the diagnosis of paraneoplastic pemphigus","volume":"43","author":"Joly","year":"2000","journal-title":"J Am Acad Dermatol"},{"key":"R3-20260501","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1159\/000051774","article-title":"Diagnosing Pemphigus foliaceus: A retrospective analysis of clinical, histological and immunological criteria","volume":"203","author":"Delmonte","year":"2001","journal-title":"Dermatology"},{"key":"R4-20260501","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.autrev.2014.01.011","article-title":"Diagnosis and classification of pemphigus and bullous pemphigoid","volume":"13","author":"Kershenovich","year":"2014","journal-title":"Autoimmun Rev"},{"key":"R5-20260501","doi-asserted-by":"crossref","first-page":"17026","DOI":"10.1038\/nrdp.2017.26","article-title":"Pemphigus","volume":"3","author":"Kasperkiewicz","year":"2017","journal-title":"Nat Rev Dis Primers"},{"key":"R6-20260501","first-page":"1085251","article-title":"Artificial intelligence in dentistry-A review","volume":"14","author":"Gokul","year":"2020","journal-title":"Indian Forensic Med Toxicol"},{"key":"R7-20260501","first-page":"e62792","article-title":"Artificial intelligence (AI)-based detection of anaemia using the clinical appearance of the gingiva","volume":"16","author":"Shubhangini","year":"2024","journal-title":"Cureus"},{"key":"R8-20260501","first-page":"3135","article-title":"Prediction of multiple recession in lower anterior using artificial intelligence","volume":"25","author":"Mohamed","year":"2024","journal-title":"South East Eur J Public Health"},{"key":"R9-20260501","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1111\/ors.12903","article-title":"Classification and prediction of smoker melanosis in gingiva using SqueezeNet algorithms","volume":"17","author":"Srinivasan","year":"2024","journal-title":"Oral Surg"},{"key":"R10-20260501","first-page":"070031","article-title":"Classification of skin cancer using artificial neural network classifier in comparison with support vector machine classifier","author":"Kumar","year":"2023","journal-title":"AIP Conference Proceedings Vol. 2821. 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