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BackgroundTemporal bone computed tomography (CT) helps diagnose chronic otitis media (COM). However, its interpretation requires training and expertise. Artificial intelligence (AI) can help clinicians evaluate COM through CT scans, but existing models lack transparency and may not fully leverage multidimensional diagnostic information. ObjectiveWe aimed to develop an explainable AI system based on 3D convolutional neural networks (CNNs) for automatic CT-based evaluation of COM. MethodsTemporal bone CT scans were retrospectively obtained from patients operated for COM between December 2015 and July 2021 at 2 independent institutes. A region of interest encompassing the middle ear was automatically segmented, and 3D CNNs were subsequently trained to identify pathological ears and cholesteatoma. An ablation study was performed to refine model architecture. Benchmark tests were conducted against a baseline 2D model and 7 clinical experts. Model performance was measured through cross-validation and external validation. Heat maps, generated using Gradient-Weighted Class Activation Mapping, were used to highlight critical decision-making regions. Finally, the AI system was assessed with a prospective cohort to aid clinicians in preoperative COM assessment. ResultsInternal and external data sets contained 1661 and 108 patients (3153 and 211 eligible ears), respectively. The 3D model exhibited decent performance with mean areas under the receiver operating characteristic curves of 0.96 (SD 0.01) and 0.93 (SD 0.01), and mean accuracies of 0.878 (SD 0.017) and 0.843 (SD 0.015), respectively, for detecting pathological ears on the 2 data sets. Similar outcomes were observed for cholesteatoma identification (mean area under the receiver operating characteristic curve 0.85, SD 0.03 and 0.83, SD 0.05; mean accuracies 0.783, SD 0.04 and 0.813, SD 0.033, respectively). The proposed 3D model achieved a commendable balance between performance and network size relative to alternative models. It significantly outperformed the 2D approach in detecting COM (P≤.05) and exhibited a substantial gain in identifying cholesteatoma (P

Abstract Background Treacher Collins syndrome (TCS; OMIM 154500) is a craniofacial developmental disorder. Methods To investigate the genetic features of a four-generation Chinese family with TCS, clinical examinations, hearing tests, computed tomography, whole-exome sequencing (WES), Sanger sequencing, reverse transcription (RT)-PCR, and the Minigene assay were performed. Results The probands, an 11-year-old male and his cousin exhibited typical clinical manifestations of TCS including conductive hearing loss, downward slanting palpebral fissures, and mandibular hypoplasia. Computed tomography revealed bilateral fusion of the anterior and posterior stapedial crura and malformation of the long crura of the incus. WES of both patients revealed a novel heterozygous intronic variant, i.e., c.4342 + 5_4342 + 8delGTGA (NM_001371623.1) in TCOF1. Minigene expression analysis revealed that the c.4342 + 5_4342 + 8delGTGA variant in TCOF1 caused a partial deletion of exon 24 (c.4115_4342del: p.Gly1373_Arg1448del), which was predicted to yield a truncated protein. The deletion was further confirmed via RT-PCR and sequencing of DNA from proband blood cells. A heterozygous variant in the POLR1C gene (NM_203290; exon6; c.525delG) was found almost co-segregated with the TCOF1 pathogenic variant. Conclusions In conclusion, we identified a heterozygous TCOF1 splicing variant c.4342 + 5_4342 + 8delGTGA (splicing) in a Chinese TSC family with ossicular chain malformations and facial anomalies. Our findings broadened the spectrum of TCS variants and will facilitate diagnostics and prognostic predictions.