Objective To develop and validate a deep learning(DL)based artificial intelligence(AI)diagnostic model for middle ear cholesteatoma and mastoiditis,as well as its potential applications to enhance diagnostic efficiency and accuracy.Methods A retrospective analysis was conducted on data from 200 patients treated for middle ear diseases at the Department of Otolaryngology,Sixth Medical Center of the PLA General Hospital,from January 2021 to August 2023,including 100 with acquired primary middle ear cholesteatoma and 100 with mastoiditis.All patients underwent preoperative high-resolution computed tomography(HRCT)of the temporal bone,and diagnoses were confirmed by sur-gical and/or pathological findings.1000 HRCT images featuring characteristic lesion changes were randomly selected for training(n=600),validation(n=100),or testing(n=300).The Convolutional Neural Networks Meet Vision Transform-ers(CMT),Efficient Vision Transformer,and Cross-Shaped Window models were employed for model training,effica-cy evaluation,and testing of best-performing models,respectively.The AI models performance was compared with those by junior,intermediate and senior level clinicians.Chi-square tests were used for statistical analysis,with a significance threshold of P=0.0125.Results The CMT model emerged as the optimal model,achieving an accuracy(ACC)of 90.0％,precision(PRE)of 90.0％,sensitivity(SEN)of 90.7％,and specificity(SPE)of 89.3％.The diagnostic accuracy of the CMT model surpassed that by junior level clinicians but was lower than that by senior level clinicians,and comparable to that by mid-level clinicians.In terms of image review time,all clinician groups were faster than the AI model.Conclu-sion The deep learning based CMT model demonstrates a significant capability to differentiate middle ear cholesteatoma from mastoiditis,offering robust diagnostic performance.
deep learningcholesteatomaotitis mediahigh-resolution CT
维普
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