背景:颞骨计算机断层扫描(CT)有助于诊断慢性中耳炎(COM)。然而,它的解释需要培训和专业知识。人工智能(AI)可以帮助临床医生通过CT扫描评估COM,但现有模型缺乏透明度,可能无法充分利用多维诊断信息.
目的:我们旨在开发一种基于3D卷积神经网络(CNN)的可解释AI系统,用于基于CT的COM自动评估。
方法:回顾性分析了2015年12月至2021年7月在2个独立机构接受COM手术的患者的颞骨CT扫描。围绕中耳的感兴趣区域被自动分割,随后对3DCNN进行了训练,以识别病理性耳朵和胆脂瘤。进行消融研究以改进模型架构。针对基线2D模型和7名临床专家进行基准测试。通过交叉验证和外部验证来测量模型性能。热图,使用梯度加权类激活映射生成,用于突出关键决策区域。最后,通过前瞻性队列对AI系统进行评估,以帮助临床医师进行术前COM评估.
结果:内部和外部数据集包含1661和108名患者(3153和211只合格耳朵),分别。3D模型表现出良好的性能,接收器工作特性曲线下的平均面积为0.96(SD0.01)和0.93(SD0.01),平均准确度为0.878(SD0.017)和0.843(SD0.015),分别,用于检测2个数据集上的病理性耳朵。胆脂瘤的鉴定结果相似(受试者工作特征曲线下的平均面积0.85,SD0.03和0.83,SD0.05;平均准确度分别为0.783,SD0.04和0.813,SD0.033)。所提出的3D模型相对于替代模型在性能和网络大小之间实现了值得称赞的平衡。它在检测COM(P≤.05)方面明显优于2D方法,并且在识别胆脂瘤方面表现出实质性的增益(P<.001)。该模型还表现出优于住院医师和主治耳科医生的诊断能力(P<0.05),在这两项任务中与所有高级临床医生相媲美。生成的热图正确突出了中耳和乳突区域,在解释颞骨CT时与人类知识保持一致。由此产生的AI系统在生成121例患者的术前诊断方面实现了81.8%的准确性,并在90.1%的病例中为临床决策做出了贡献。
结论:我们提出了一个3DCNN模型,通过颞骨CT扫描来检测病理变化并识别胆脂瘤。在这两项任务中,该模型明显优于基线2D方法,达到与人类专家相当或超过人类专家的水平。该模型还表现出良好的泛化性和增强的可理解性。该AI系统有助于自动COM评估,并在现实世界的临床环境中显示出有希望的可行性。这些发现强调了AI在COM评估中作为临床医生有价值的辅助手段的潜力。
背景:中国临床试验注册中心ChiCTR2000036300;https://www.chictr.org.cn/showprojEN.html?proj=58685。
BACKGROUND: Temporal 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.
OBJECTIVE: We aimed to develop an explainable AI system based on 3D convolutional neural networks (CNNs) for automatic CT-based evaluation of COM.
METHODS: Temporal 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.
RESULTS: Internal 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<.001). The model also demonstrated superior diagnostic capabilities over resident fellows and the attending otologist (P<.05), rivaling all senior clinicians in both tasks. The generated heat maps properly highlighted the middle ear and mastoid regions, aligning with human knowledge in interpreting temporal bone CT. The resulting AI system achieved an accuracy of 81.8% in generating preoperative diagnoses for 121 patients and contributed to clinical decision-making in 90.1% cases.
CONCLUSIONS: We present a 3D CNN model trained to detect pathological changes and identify
cholesteatoma via temporal bone CT scans. In both tasks, this model significantly outperforms the baseline 2D approach, achieving levels comparable with or surpassing those of human experts. The model also exhibits decent generalizability and enhanced comprehensibility. This AI system facilitates automatic COM assessment and shows promising viability in real-world clinical settings. These findings underscore AI\'s potential as a valuable aid for clinicians in COM evaluation.
BACKGROUND: Chinese Clinical Trial Registry ChiCTR2000036300; https://www.chictr.org.cn/showprojEN.html?proj=58685.