Title
Authors
Nupur Dhanak1, Vaibhav T Chougule2,*, Keerthi Nalluri3, Ankur Kakkad4, Ankit Dhimole5 & Anuj Singh Parihar6
Affiliation
1Department of Conservative Dentistry and Endodontics, Government Dental College and Hospital, Ahmadabad, Gujarat, India; 2Department of Paediatric and Preventive Dentistry, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, Maharashtra, India; 3Apex, North Carolina, USA; 4,5Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, MP, India; 6Department of Periodontology, People's Dental Academy, Bhopal, Madhya Pradesh, India; *Corresponding author; Communicated by Anil Kumar - E-mail: anilkk44@gmail.com
Nupur Dhanak - E-mail: bhavsarnupur@yahoo.co.in; Phone: +91 - 9428198077
Vaibhav T Chougule - E-mail: vbc4866@gmail.com; Phone: +91 - 9986155301
Keerthi Nalluri - E-mail: - keerthi.nalluri6@gmail.com; Phone: +91 - 7259998190
Ankur Kakkad - E-mail: drkakkadomdr@gmail.com; Phone: +91 - 9827783319
Ankit Dhimole - E-mail: drannkit@gmail.com; Phone: +91 - 7869543666
Anuj Singh Parihar - E-mail: dr.anujparihar@gmail.com; Phone: +91 - 8827047003
Article Type
Research Article
Date
Received March 1, 2024; Revised March 31, 2024; Accepted March 31, 2024, Published March 31, 2024
Abstract
Diagnosis of proximal caries is a difficult task. Artificial intelligence (AI) enabled diagnosis is gaining momentum. Therefore, it is of interest to evaluate the effectiveness of an artificial intelligence (AI) smart phone application for bitewing radiography towards real-time caries lesion detection. The Efficient Det-Lite1 artificial neural network was used after training 100 radiographic images obtained from the department of Oral Medicine. Trained model was then installed in a Google Pixel 6 (GP6) smartphone as artificial intelligence app. The back-facing mobile phone video camera of GP6 was utilised to detect caries lesions on 100 bitewing radiographs (BWR) with 80 carious lesion in real-time. Two different techniques such as scanning the static BWR on laptop with a moving mobile and scanning the moving radiograph on the laptop with stationery mobile were used. The average value of sensitivity/precision/F1 scores for both the techniques was 0.75/0.846 and 0.795 respectively. AI programme using the rear-facing mobile phone video camera was found to detect 75% of caries lesions in real time on 100 BWR with a precision of 84.6%. Thus, the use of AI with smart phone app is useful for caries diagnosis which is readily accessible, easy to use and fast.
Keywords
Artificial intelligence, bitewing radiograph, caries detection, mobile phone
Citation
Dhanak et al. Bioinformation 20(3): 243-247 (2024)
Edited by
Peter N Pushparaj
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
