{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T02:20:17Z","timestamp":1785464417102,"version":"3.56.0"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2\u2013Class, 3\u2013Class, and 5\u2013Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2\u2013class, 66% for 3\u2013class, and 42.8% for 5\u2013Class. The RF showed the highest accuracy of 47.6% for 5\u2013Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.<\/jats:p>","DOI":"10.3390\/jimaging9020033","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T02:57:24Z","timestamp":1675306644000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks"],"prefix":"10.3390","volume":"9","author":[{"given":"Vathsala","family":"Patil","sequence":"first","affiliation":[{"name":"Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janhavi","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravindranath","family":"Vineetha","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1491-1166","authenticated-orcid":false,"given":"Rahul","family":"Paul","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5021-4029","authenticated-orcid":false,"given":"Dasharathraj K.","family":"Shetty","sequence":"additional","affiliation":[{"name":"Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonali","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Komal","family":"Smriti","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak Kumar","family":"Singhal","sequence":"additional","affiliation":[{"name":"Department of Public Health Dentistry, Manipal College of Dental Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0356-7697","authenticated-orcid":false,"given":"Nithesh","family":"Naik","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"},{"name":"Curiouz TechLab Private Limited, BIRAC-BioNEST, Manipal Government of Karnataka Bioincubator, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6761476","DOI":"10.1155\/2022\/6761476","article-title":"The Applicable Dental Age Estimation Methods for Children and Adolescents in Indonesia","volume":"2022","author":"Kurniawan","year":"2022","journal-title":"Int. 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