Mattingly, Jameson K.

AbstractObjectiveThis 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.Study DesignClinician diagnostic reader study.SettingOnline diagnostic survey of ENT physicians.MethodsNine 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.ResultsThe 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.ConclusionThis 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.

Abstract Objective The main purpose of this article is to determine if vestibular schwannoma consistency as determined by tissue intensity on T2-weighted magnetic resonance imagings (MRIs) is predictive of intraoperative experience and postoperative clinical outcomes. Study Design Retrospective chart review. Setting Tertiary referral center. Patients Seventy-seven patients diagnosed with vestibular schwannomas who were treated with microsurgical resection. Intervention Diagnostic. Main Outcome Measures Intraoperative measures include totality of resection, surgical time and cranial nerve VII stimulation and postoperative measures include House–Brackmann grade and perioperative complications. Results Tumor consistency determined via tissue intensity on MRI was only found to correlate with surgical time, with a softer tumor being associated with a longer surgical time (p < 0.0001). However, this was primarily driven by tumor volume with larger tumors being associated with longer surgical time based on multivariate analysis. None of the other intraoperative or postoperative measures considered were found to correlate with tumor consistency. Conclusions Tumor consistency determined by MRI is not predictive of intraoperative experience or postoperative outcomes in vestibular schwannomas. Tumor volume is the strongest driver of these outcome measures as opposed to tumor consistency.