目的:本研究调查了耳朵的比较性能,鼻子,和喉部(ENT)医生在使用3种不同的可视化方法检查数字耳镜成像时正确检测耳朵异常,包括计算机辅助合成图像,称为“SelectStitch”,\"单个视频帧\"静止\"图像,和视频剪辑。该研究还探讨了临床医生的诊断信心水平和做出诊断的时间。
方法:临床医生诊断读者研究。
方法:耳鼻喉科医生的在线诊断调查。
方法:九位耳鼻喉科医生回顾了86只耳朵的数字耳镜检查,并进行了各种诊断(正常,穿孔,撤回,中耳积液,鼓室硬化)。耳镜检查使用基于人工智能(AI)的计算机辅助合成图像从视频剪辑(SelectStitch)生成,从视频剪辑中手动选择最佳静止帧(静止),或整个视频剪辑。统计分析包括检测正确诊断的能力比较,信心水平,和诊断时间。
结果:耳鼻喉科医生检测耳朵异常的能力(33.2%-68.7%)因病理而异。SelectStitch和Still图像在检测异常方面没有统计学差异(P>.50),但两者都不同于视频(P<0.01)。然而,使用Videos观察到的性能改善是以确定诊断的时间明显延长为代价的.诊断的信心水平与正确诊断呈正相关,但因特定的病理学而不同。
结论:这项研究探索了计算机辅助技术如SelectStitch在增强耳镜诊断和节省时间方面的潜力,这可能有利于远程医疗设置。计算机生成的图像和手动选择的图像之间的可比性能表明AI算法在耳镜检查应用中的潜力。
OBJECTIVE: This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called \"SelectStitch,\" single video frame \"Still\" images, and video clips. The study also explored clinicians\' diagnostic confidence levels and the time to make a diagnosis.
METHODS: Clinician diagnostic reader study.
METHODS: Online diagnostic survey of ENT physicians.
METHODS: Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image generation from a video clip (SelectStitch), manually selected best still frame from a video clip (Still), or the entire video clip. Statistical analyses included comparisons of ability to detect correct diagnosis, confidence levels, and diagnosis times.
RESULTS: The ENT physicians\' ability to detect ear abnormalities (33.2%-68.7%) varied depending on the pathologies. SelectStitch and Still images were not statistically different in detecting abnormalities (P > .50), but both were different from Video (P < .01). However, the performance improvement observed with Videos came at the cost of significantly longer time to determining the diagnosis. The level of confidence in the diagnosis was positively associated with correct diagnoses, but varied by particular pathology.
CONCLUSIONS: This study explores the potential of computer-assisted techniques like SelectStitch in enhancing otoscopic diagnoses and time-saving, which could benefit telemedicine settings. Comparable performance between computer-generated and manually selected images suggests the potential of AI algorithms for otoscopy applications.