Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Abstract

In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases

“…This system used a machine learning algorithm and support vector machine (SVM), k-nearest neighbor (k-NN), and decision trees with a 720-case training set. The group differentiated ear wax plug, myringosclerosis, chronic otitis media, and normal tympanic membrane with an accuracy of 93.8% [ 17 ]. Another study proposed a diagnostic system based on deep convolutional neural networks that achieved an average accuracy of 93.6%.…”

Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

“…The use of machine learning algorithms applied to different medical data has increased in the last decade and, in particular, those applications related with medical images (De Fauw et al, 2018 ; Rajpurkar et al, 2018 ; Elaziz et al, 2020 ; Stemmer et al, 2020 ). However, machine learning techniques arise as a powerful tool to analyses data coming from different sources (Corey et al, 2018 ; Fontanella et al, 2018 ), in order to assist clinicians in the diagnosis stage (Kim et al, 2017 ; Viscaino et al, 2020 ). Moreover, a previous study described a machine learning model based on smooth pursuit data that discriminated age-matched controls from young-onset Alzheimer's disease patients with ~95% accuracy (Pavisic et al, 2017 ).…”

Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

“…Hence, the ability to automate diagnosis through machine learning has garnered considerable interest. Multiple studies have applied machine learning to visible light otoscopy for identification chronic otitis media with perforation, cerumen, myringosclerosis, and retraction pockets 45 , 62 – 64 . Livingstone and colleagues uploaded visible light otoscopy images into a Google Cloud automated algorithm, AutoML, and compared the diagnostic accuracy of the algorithm to physicians from a variety of specialties 44 .…”

“…Deep learning techniques are also widely used for image processing and classification 38 – 42 . In recent years, investigators have studied machine learning to automate diagnosis in the field of otolaryngology, especially with respect to otitis media 43 – 45 .…”