Building an Otoscopic screening prototype tool using deep learning

Abstract

BackgroundOtologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network.Material and methodsA database of 734 unique otoscopic images of various ear pathologies, including 63 cerumen impactions, 120 tympanostomy tubes, and 346 normal tympanic membranes were acquired. 80% of the images were used for the training of a convolutional neural network and the remaining 20% were used for algorithm validation. Image augmentation was employed on the training dataset to increase the number of training images. The general network architecture consisted of three convolutional layers plus batch normalization and dropout layers to avoid over fitting.ResultsThe validation based on 45 datasets not used for model training revealed that the proposed deep convolutional neural network is capable of identifying and differentiating between normal tympanic membranes, tympanostomy tubes, and cerumen impactions with an overall accuracy of 84.4%.ConclusionOur study shows that deep convolutional neural networks hold immense potential as a diagnostic adjunct for otologic disease management.

Deep learning-enabled medical computer vision

“…CV in medical modalities with non-standardized data collection requires the integration of CV into existing physical systems. For instance, in otolaryngology, CNNs can be used to help primary care physicians manage patients’ ears, nose, and throat 40 , through mountable devices attached to smartphones 41 . Hematology and serology can benefit from microscope-integrated AIs 42 that diagnose common conditions 43 or count blood cells of various types 44 —repetitive tasks that are easy to augment with CNNs.…”

A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images

“…Developments in artificial intelligence (AI) and increases in available annotated medical data have allowed for successful application of these technologies to many fields of medicine (7)(8)(9). A subset of AI, deep learning, is particularly suited for image classification and segmentation tasks and has recently been applied for making diagnoses based on TM images taken via otoscopes (10,11). Such technology has significant potential for supporting proper patient care in regions lacking in medical resources by outputting automatic diagnoses.…”

Otoscopy video screening with deep anomaly detection

“…The Cellscope Oto device is designed to attache to an ear speculum and provides an additional light source but relies on the iPhone camera to capture the exam. We do note that the image and video quality captured on this device is not as high as reported in previous works [6], [41], especially with respect to as sharpness and clarity. Unlike Cellscope oto however more advanced high quality otoscopes with digital video capacity (as used in [6], [41]) are often cost prohibitive to wider adoption.…”