Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study

Abstract

Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current examination cannot sufficiently predict the progression before surgery, and changes in approach may be made during the surgery. Large amounts of data are typically required to train deep neural network models; however, the prevalence of cholesteatomas is low (1-in-25, 000). Developing analysis methods that improve the accuracy with such a small number of samples is an important issue for medical artificial intelligence (AI) research. This paper presents an AI-based system to automatically detect mastoid extensions using CT. This retrospective study included 164 patients (80 with mastoid extension and 84 without mastoid extension) who underwent surgery. This study adopted a relatively lightweight neural network model called MobileNetV2 to learn and predict the CT images of 164 patients. The training was performed with eight divided groups for cross-validation and was performed 24 times with each of the eight groups to verify accuracy fluctuations caused by randomly augmented learning. An evaluation was performed by each of the 24 single-trained models, and 24 sets of ensemble predictions with 23 models for 100% original size images and 400% zoomed images. Fifteen otolaryngologists diagnosed the images and compared the results. The average accuracy of predicting 400% zoomed images using ensemble prediction model was 81.14% (sensitivity = 84.95%, specificity = 77.33%). The average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%), which was not affected by their clinical experiences. Noteworthily, despite the small number of cases, we were able to create a highly accurate AI. These findings represent an important first step in the automatic diagnosis of the cholesteatoma extension.

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.…”

“…This may lead to a failure to fit the data from other centers. Takahashi et al 71 included 164 patients (80 with mastoid extension and 84 without) to build an AI‐based system to detect mastoid extensions using CT automatically. The average accuracy of the model was 81.14% (sensitivity, 84.95%; specificity, 77.33%), while the average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%).…”

“…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.…”

Preoperative prediction of sinonasal papilloma by artificial intelligence using nasal video endoscopy: A retrospective study

“…Nevertheless, the AI studies we performed, one of which used hysteroscopic videos and another predicted the extent of middle ear cholesteatoma development, demonstrated high accuracy with a small sample size. 2,3 IP occurs in approximately 1.5 cases per 100,000 people annually, 4 recurs in approximately 15% of cases, and becomes malignant in approximately 5% of cases. 1 IP may have a characteristic "raspberry-like"…”