■文献中缺少有关牙医与牙医的比较的信息基于诊断能力的人工智能(AI)。这项研究的目的是通过将AI软件与具有1或2年经验的初级牙医进行比较,以评估基于有关龋齿和根尖感染检测的放射学诊断的诊断性能。根据专业牙医的有效决定。
■在研究的初始阶段,2位专业牙医在500张数字全景射线照片上评估了龋齿和根尖周病变的存在,并以秒为单位记录检测时间。在第二阶段,3名初级牙医和一个人工智能软件在相同的全景射线照片上进行了诊断,并以秒为单位记录诊断结果和持续时间.
■AI和三个初级牙医,分别,检测到龋齿的灵敏度(SEN)为0.907、0.889、0.491、0.907;特异性(SPEC)为0.760、0.740、0.454、0.696;阳性预测值(PPV)为0.693、0.470、0.155、0.666;阴性预测值(NPV)为0.505、0.415、0.275、0.367,F1评分为0.786、0.615、0.236、AI和三名初级牙医分别检测到SEN为0.973、0.962、0.758、0.958的根尖病变;SPEC为0.629、0.421、0.404、0.621;PPV为0.861、0.651、0.312、0.648;NPV为0.689、0.673、0.278、0.546,F1评分为0.914、0.777、0.442、0.773。AI软件给出了更准确的结果,特别是在检测根尖周病变方面。另一方面,在龋齿检测中,AI和初级牙医的低诊断率都很高。
■关于所需的评估时间,AI执行得更快,平均而言。
UNASSIGNED: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists.
UNASSIGNED: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds.
UNASSIGNED: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists.
UNASSIGNED: Regarding the evaluation time needed, AI performed faster, on average.