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

Autor: Viscaino M; Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile., Maass JC; Interdisciplinary Program of Phisiology and Biophisics, Facultad de Medicina, Instituto de Ciencias Biomedicas, Universidad de Chile, Santiago, Chile.; Department of Otolaryngology, Hospital Clínico de la Universidad de Chile, Santiago, Chile., Delano PH; Department of Neuroscience, Facultad de Medicina, Universidad de Chile, Santiago, Chile.; Department of Otolaryngology, Hospital Clínico de la Universidad de Chile, Santiago, Chile., Torrente M; Department of Otolaryngology, Hospital Clínico de la Universidad de Chile, Santiago, Chile., Stott C; Department of Otolaryngology, Hospital Clínico de la Universidad de Chile, Santiago, Chile., Auat Cheein F; Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Mar 12; Vol. 15 (3), pp. e0229226. Date of Electronic Publication: 2020 Mar 12 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0229226
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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