The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography

Abstract

Background: The purpose of this study was to explore the common characteristics of fenestral otosclerosis (OS) which are misdiagnosed, and develop a deep learning model for the diagnosis of fenestral OS based on temporal bone high-resolution computed tomography scans. Methods:We conducted a study to explicitly analyze the clinical performance of otolaryngologists in diagnosing fenestral OS and developed an explainable deep learning model using 134,574 temporal bone high-resolution computed tomography (HRCT) slices collected from 1,294 patients for the automatic diagnosis of fenestral OS. We prospectively created an external test set with 31,774 CT slices from 144 patients, which contained 86 fenestral OS ears and 202 normal ears and used it to evaluate the performance of our otosclerosis-Logical Neural Network (LNN) model to assess its potential clinical utility. In addition, we compared the diagnostic acumen of seven otolaryngologists with the otosclerosis-LNN approach in the clinical test set, which was mixed with 78 fenestral OS and 62 normal ears. Finally, to evaluate the assisting value of the model, the seven participants were again invited to classify all cases in the clinical test set after referring to the diagnostic results of the model, to which they were blinded. Results:The diagnostic performance of otologists was not satisfactory, and those CT samples which were misdiagnosed had similar characteristics. Based on this finding, we defined three subtypes of fenestral OS lesions that are suitable for clinical diagnosis guidance: "focal", "transitional", and "typical" fenestral OS.The most encouraging result is that the model achieved an area under the curve (AUC) of 99.5% (per-earsensitivity of 96.4%, per-ear-specificity of 98.9%) on the prospective unknown external test. Furthermore, we used this model to assist otologists and observed a consistent and significant improvement in diagnostic performance, especially for the newly defined focal and transitional fenestral OS, which led to the initial high misdiagnosis rate.Conclusions: Our findings of the fine-grained classification of fenestral OS could have implications for

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

“…The 340 articles that did not mention XAI models or clinical use in the title or abstract were excluded during the title/abstract screening process. In five studies, the aim was to develop an explanatory model to assist healthcare providers in diagnosing a patient's disease [ [29] , [30] , [31] , [32] , [33] ]. The aim of the remaining study was to examine the explainability of the model in order to identify variables that have an important influence on the prediction results [ 34 ].…”

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…Several studies have directly compared AI against human physicians 24,26,31,32,36,42,52,71 . Some showed the superiority of AI over nonspecialist physicians 26 or otolaryngologists 71 in narrowly defined fields.…”

“…Recent studies have applied AI to otological imaging in various clinical contexts (Supplemental File 3, available online). These studies combined AI with otoscopy, [22][23][24][25][26]31,32,38,48,52,57,68,[74][75][76]79,81,84,85,93,95 computed tomography (CT), 30,36,41,42,50,62,63,70,71,88 and magnetic resonance imaging (MRI). 43,73,78 Most studies have focused on the image-based otoscopic diagnosis and automated segmentation of temporal bone CT for classifying normal and abnormal mastoid air cells.…”

“…73,78 Several studies have directly compared AI against human physicians. 24,26,31,32,36,42,52,71 Some showed the superiority of AI over nonspecialist physicians 26 or otolaryngologists 71 in narrowly defined fields. Some studies used a single AI algorithm, while others combined 2 or more algorithms to solve clinical problems.…”

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

“…By using LIME, the model could provide visual cues for clinician. Tan et al presented an otosclerosis-logical neural network (LNN) on temporal high-resolution computed tomography (HRCT) bone slices for fenestral otosclerosis diagnosis [ 31 ]. The proposed method achieved an AUC of 99.5% on the external test dataset.…”