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Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.人工智能在放射影像学龋病检测中的应用:系统评价。
BMC Oral Health. 2024 Feb 24;24(1):274. doi: 10.1186/s12903-024-04046-7.
2
Automatic caries detection in bitewing radiographs-Part II: experimental comparison.咬合翼片X线片中龋齿的自动检测——第二部分:实验比较
Clin Oral Investig. 2024 Feb 5;28(2):133. doi: 10.1007/s00784-024-05528-2.
3
Automatic caries detection in bitewing radiographs: part I-deep learning.口腔颌面全景片中龋齿的自动检测:第一部分-深度学习。
Clin Oral Investig. 2023 Dec;27(12):7463-7471. doi: 10.1007/s00784-023-05335-1. Epub 2023 Nov 16.
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Correction to: Evaluation of radix entomolaris in mandibular first and second molars using cone-beam computed tomography and review of the literature.对《使用锥形束计算机断层扫描评估下颌第一和第二磨牙的磨牙后根及文献综述》的更正
Oral Radiol. 2022 Jul;38(3):443. doi: 10.1007/s11282-022-00609-y.
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Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries.不同机器学习算法在龋齿诊断中的应用。
J Healthc Eng. 2022 Mar 31;2022:5032435. doi: 10.1155/2022/5032435. eCollection 2022.
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Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks.数字龋病学中的人工智能:一种使用卷积神经网络诊断深龋和牙髓炎的新工具。
Ann Transl Med. 2021 May;9(9):763. doi: 10.21037/atm-21-119.
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Classification of caries in third molars on panoramic radiographs using deep learning.基于深度学习的曲面体层片第三磨牙龋病分类。
Sci Rep. 2021 Jun 15;11(1):12609. doi: 10.1038/s41598-021-92121-2.
8
Detecting caries lesions of different radiographic extension on bitewings using deep learning.使用深度学习检测牙尖片上不同放射学延伸龋损。
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Health Inf Sci Syst. 2020 Jan 3;8(1):8. doi: 10.1007/s13755-019-0096-y. eCollection 2020 Dec.
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基于人工智能的智能手机应用程序,用于在咬合翼片X光片上进行实时龋齿检测。

Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs.

作者信息

Dhanak Nupur, Chougule Vaibhav T, Nalluri Keerthi, Kakkad Ankur, Dhimole Ankit, Parihar Anuj Singh

机构信息

Department of Conservative Dentistry and Endodontics, Government Dental College and Hospital, Ahmadabad, Gujarat, India.

Department of Paediatric and Preventive Dentistry, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, Maharashtra, India.

出版信息

Bioinformation. 2024 Mar 31;20(3):243-247. doi: 10.6026/973206300200243. eCollection 2024.

DOI:10.6026/973206300200243
PMID:38711998
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11069605/
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.

摘要

近端龋齿的诊断是一项艰巨的任务。人工智能(AI)辅助诊断正日益受到关注。因此,评估一款用于咬合翼片X线摄影的人工智能(AI)智能手机应用程序在实时检测龋齿病变方面的有效性具有重要意义。在对从口腔医学科获取的100张X线影像进行训练后,使用了高效Det-Lite1人工神经网络。然后,将训练好的模型作为人工智能应用程序安装在谷歌像素6(GP6)智能手机中。利用GP6的后置手机摄像头实时检测100张咬合翼片X线片(BWR)上的龋齿病变,其中有80处龋齿病变。使用了两种不同的技术,即通过移动的手机在笔记本电脑上扫描静态BWR以及通过固定的手机在笔记本电脑上扫描移动的X线片。两种技术的灵敏度/精度/F1分数的平均值分别为0.75/0.846和0.795。发现使用后置手机摄像头的AI程序在100张BWR上实时检测到75%的龋齿病变,精度为84.6%。因此,将AI与智能手机应用程序结合使用对龋齿诊断很有用,这种方式易于获取、使用方便且速度快。