West China Journal of Stomatology ›› 2022, Vol. 40 ›› Issue (5): 576-581.doi: 10.7518/hxkq.2022.05.011

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Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging

Qian Jun1(), Ma Rui1, Qu Yan2, Deng Shaochun1, Duan Yao1, Zuo Feifei3, Wang Yajie3, Wu Yuwei1()   

  1. 1.Second Clinical Division, Peking University School and Hospital of Stomatology; National Clinical Research Center for Oral Diseases; National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing 100020, China
    2.Dept. of Stomatology, Beijing Rehabilitation Hospital of Capital Medical University, Beijing 100144, China
    3.LargeV Instrument Corp. Ltd, Beijing 100084, China
  • Received:2022-04-13 Revised:2022-07-05 Online:2022-10-01 Published:2022-10-17
  • Contact: Wu Yuwei E-mail:atlasatlas@163.com;yuweiwu@bjmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(81300851);Research of Capital Health Security and Cultivation(Z181100001618018);Correspondence: Wu Yuwei, E-mail: yuweiwu@bjmu.edu.cn


Objective This study aims to investigate the diagnostic application of an artificial intelligence (AI) computer-aided diagnostic system based on a convolutional neural network algorithm in detecting chronic apical periodontitis in cone beam computed tomography (CBCT) images. Methods CBCT raw data of 55 single root chronic apical pe-riodontitis taken in 2nd Dental Center of Peking University School and Hospital from 49 patients from January 2017 to December 2021 were collected, and the chronic apical periodontitis areas were identified by experienced clinicians ma-nually and segmented layer by layer in Materialise Mimics Medical Software. Deep learning of lesion characterization was conducted via AI 3D U-Net, and the network segmentation results were compared manually with the test sets in terms of intersection over union (IOU), Dice coefficient, and pixel accuracy (PA). Results In our deep learning algorithm, the IOU for all actual true lesions in test set samples was 92.18%, and the Dice coefficient and the PA index were 95.93% and 99.27%, respectively. Lesion segmentation and volume measurements performed by humans and AI systems showed excellent agreement. Conclusion AI systems based on deep learning methods can be applied for detecting chronic apical periodontitis on CBCT images in clinical applications.

Key words: artificial intelligence, cone-beam computed tomography, deep learning, chronic apical periodontitis

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