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

• Published in: PloS one Vol. 15; no. 3; p. e0229226
• Main Authors: Viscaino, Michelle, Maass, Juan C, Delano, Paul H, Torrente, Mariela, Stott, Carlos, Auat Cheein, Fernando
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12-03-2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Banks (Finance); Biology and Life Sciences; Cancer; Classification; Computer aided testing; Computer and Information Sciences; Data mining; Datasets; Decision trees; Diagnosis; Diagnostic errors; Discrete cosine transform; Ear; Ear canal; Ear diseases; Engineering and Technology; Feature extraction; Filter banks; General practitioners; Health care; Health care costs; Hospitals; Image processing; Learning algorithms; Machine learning; Medical diagnosis; Medical economics; Medical imaging; Medical imaging equipment; Medicine and Health Sciences; Middle ear; Model accuracy; Neural networks; Otitis; Otitis media; Otolaryngology; Patients; People and Places; Physical Sciences; Research and Analysis Methods; Smartphones; Studies; Support vector machines; Training; Tympanic membrane; Chile
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0229226

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.