Das Maneesha, Shahnawaz Kamil, Raghavendra Koti, Kavitha R, Nagareddy Bharath, Murugesan Sabari
Department of Conservative Dentistry and Endodontics, Institute of Dental Sciences, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India.
Department of Conservative and Endodontics, Darbhanga Medical College, Laheriasarai, Darbhanga, Bihar, India.
J Pharm Bioallied Sci. 2024 Feb;16(Suppl 1):S812-S814. doi: 10.4103/jpbs.jpbs_1029_23. Epub 2024 Feb 29.
Dental caries is a prevalent oral health issue, often diagnosed through intraoral radiographs. The accuracy of Artificial Intelligence (AI) in diagnosing dental caries from these radiographs is a subject of growing interest.
In this RCT, 200 intraoral radiographs were collected from patients seeking dental care. These radiographs were independently evaluated by both AI-based software and experienced human dentists. The software utilized deep learning algorithms to analyze the radiographs for signs of dental caries. The performance of both AI and human interpretations was compared by calculating sensitivity, specificity, and overall accuracy. Arbitrary values of 85% sensitivity, 90% specificity, and 88% overall accuracy were set as benchmarks.
The AI-based software demonstrated a sensitivity of 88%, a specificity of 91%, and an overall accuracy of 89% in diagnosing dental caries from intraoral radiographs. Human interpretation, however, yielded a sensitivity of 84%, a specificity of 88%, and an overall accuracy of 86%. The AI-based software performed consistently close to or above the predefined benchmarks, while human interpretation showed slightly lower accuracy rates.
This RCT suggests that AI-based software is a valuable tool for diagnosing dental caries from intraoral radiographs, with performance comparable to or exceeding that of experienced human dentists. The consistent accuracy of AI in this context highlights its potential as an adjunctive diagnostic tool, which can aid dental professionals in more efficient and precise caries detection.
龋齿是一个普遍存在的口腔健康问题，通常通过口腔内X光片进行诊断。人工智能（AI）从这些X光片中诊断龋齿的准确性是一个越来越受关注的话题。
在这项随机对照试验中，从寻求牙科护理的患者中收集了200张口腔内X光片。这些X光片由基于AI的软件和经验丰富的人类牙医独立评估。该软件利用深度学习算法分析X光片以寻找龋齿迹象。通过计算敏感性、特异性和总体准确性来比较AI和人类解读的性能。将85%的敏感性、90%的特异性和88%的总体准确性的任意值设定为基准。
基于AI的软件在从口腔内X光片中诊断龋齿时，敏感性为88%，特异性为91%，总体准确性为89%。然而，人类解读的敏感性为84%，特异性为88%，总体准确性为86%。基于AI的软件的表现始终接近或高于预定义的基准，而人类解读的准确率略低。
这项随机对照试验表明，基于AI的软件是从口腔内X光片中诊断龋齿的有价值工具，其性能与经验丰富的人类牙医相当或超过人类牙医。在这种情况下，AI的一致准确性凸显了其作为辅助诊断工具的潜力，可帮助牙科专业人员更高效、精确地检测龋齿。
