Camalan Seda, Langefeld Carl D, Zinnia Amy, McKee Brigham, Carlson Matthew L, Deep Nicholas L, Harris Michael S, Jan Taha A, Kaul Vivian F, Lindquist Nathan R, Mattingly Jameson K, Shah Jay, Zhan Kevin Y, Gurcan Metin N, Moberly Aaron C
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Department of Biostatistics and Data Science, Bowman Gray Center for Medical Education, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Otolaryngol Head Neck Surg. 2025 Jan;172(1):152-161. doi: 10.1002/ohn.965. Epub 2024 Sep 2.
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.
Clinician diagnostic reader study.
Online diagnostic survey of ENT physicians.
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.
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.
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.
本研究调查了耳鼻喉科（ENT）医生在使用三种不同可视化方法（包括称为“SelectStitch”的计算机辅助合成图像、单视频帧“静止”图像和视频片段）查看数字耳镜成像时正确检测耳部异常的比较表现。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。
临床医生诊断性阅片研究。
对耳鼻喉科医生进行在线诊断调查。
九名耳鼻喉科医生对来自86只耳朵的数字耳镜检查进行了评估，这些耳朵有各种诊断结果（正常、穿孔、内陷、中耳积液、鼓室硬化）。耳镜检查使用了基于人工智能（AI）的计算机辅助合成图像生成技术，该技术从视频片段中生成（SelectStitch），从视频片段中手动选择最佳静止帧（静止），或使用整个视频片段。统计分析包括对检测正确诊断的能力、信心水平和诊断时间的比较。
耳鼻喉科医生检测耳部异常的能力（33.2%-68.7%）因病理情况而异。SelectStitch和静止图像在检测异常方面无统计学差异（P>.50），但两者均与视频不同（P<.01）。然而，使用视频观察到的性能提升是以显著延长诊断时间为代价的。诊断信心水平与正确诊断呈正相关，但因具体病理情况而异。
本研究探讨了SelectStitch等计算机辅助技术在增强耳镜诊断和节省时间方面的潜力，这可能有益于远程医疗环境。计算机生成图像和手动选择图像之间的可比性能表明了人工智能算法在耳镜检查应用中的潜力。
