Deep learning

Internal medicine

Radiology

Pattern recognition (psychology)

Middle ear

Gene

Otorhinolaryngology

Pharmacokinetics

Machine learning

Surgery

Artificial intelligence

Chemistry

Cholesteatoma

Area under curve

Computer science

Computed tomography

Receiver operating characteristic

Biochemistry

Robustness (evolution)

Medicine

Classifier (UML)

Expert Systems with Applications

The main purpose of this paper was to develop a deep-learning method for the diagnosis of different chronic middle ear diseases, including middle ear cholesteatoma and chronic suppurative otitis media, based on computed tomography (CT) images of the middle ear. The origin of the dataset was the CT scans of 499 patients, which included both ears and selected by specialized otologists. The final dataset was constructed from 973 ears, which labeled by a professional otolaryngologist and classified into 3 conditions: MEC, CSOM and normal. The diagnostic framework, called the "Middle Ear Structure Identification Classifier"(MESIC), was consisted of two deep-learning networks with dissimilar functions: a "region of interest" area search network for extracting the special image of the middle ear structure and a classification network for finishing the diagnosis. The area under the curve (AUC), which means receiver operating characteristic curve (ROC), reflects the robustness of the algorithm by comparing its sorting effectiveness. According to simulation experiments, we chose Visual Geometry Group 16 (VGG-16) as the model's backbone. In our framework, the ROI search part exhibited an AUC of 0.99 on the right and 0.98 on the left. The classification part exhibited an average AUC of 0.96 for both sides based on VGG-16. The average precision (90.1%), recall (85.4%) and F1-score (87.2%) show the effectiveness of framework. This paper presents a deep-learning framework to automatically diagnose cholesteatoma and CSOM. The results show that MESIC can effectively and quickly classify these two common diseases through CT images, which can ameliorate the pressure of professional doctors and the practical problems of the lack of professional doctors in rural areas.

Structure-aware deep learning for chronic middle ear disease