{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:54:45Z","timestamp":1770742485321,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European University of the Atlantic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.<\/jats:p>","DOI":"10.3390\/s23156837","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"6837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19"],"prefix":"10.3390","volume":"23","author":[{"given":"Imran","family":"Shafi","sequence":"first","affiliation":[{"name":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Muhammad","family":"Sajad","sequence":"additional","affiliation":[{"name":"Abasyn University Islamabad Campus, Islamabad 44000, Pakistan"}]},{"given":"Anum","family":"Fatima","sequence":"additional","affiliation":[{"name":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Daniel Gavilanes","family":"Aray","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Universidad Internacional Iberoamericana, Campeche 24560, Mexico"},{"name":"Fundaci\u00f3n Universitaria Internacional de Colombia Bogot\u00e1, Bogot\u00e1 11131, Colombia"}]},{"given":"Viv\u00edan","family":"Lipari","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA"},{"name":"Universidade Internacional do Cuanza, Cuito EN250, Bi\u00e9, Angola"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel de la Torre","family":"Diez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Bel\u00e9n, 15, 47011 Valladolid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Dentistry 4.0: A Whole New Paradigm","volume":"4","author":"Tagra","year":"2021","journal-title":"Dicoveries Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. 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