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.

Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.

Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.

Ear disorders are among the most common diseases treated in primary care, with a high percentage of non-relevant referrals. The conventional diagnostic procedure is done by a visual examination of the ear canal and tympanic membrane. Consequently, the accuracy of the diagnosis is affected by observer-observer variation, depending on the technical skill and experiences of the physician as well as on the subjective bias of the observer. This situation impacts the proper implementation of treatments, increases health costs, and can lead to serious health complications. To eliminate subjectivity and enhance diagnostic accuracy, we present a diagnostic tool for nine ear conditions in a computer-aided diagnosis scheme. We propose a hybrid learning framework based on convolutional and recurrent neural networks for video otoscopy analysis. The proposed method first extracts the deep features of each relevant frame from the video. Then, a Long Short-term Memory network is introduced to learn spatial sequential data by analyzing deep features for a certain time interval. We carried out the study in collaboration with the Clinical Hospital of the University of Chile and included 875 subjects in a period of 12 months (continuous). The experiments were conducted on a new video otoscopy dataset and showed high performance in terms of accuracy (98.15%), precision (91.94%), sensitivity (91.67%), specificity (98.96%), and F1-score (91.51%). To the best of our knowledge, the proposed system is capable of predicting more diagnoses of ear conditions known to date with high performance. Our system is designed to assist in a real otoscopy examination by analyzing a sequence of images instead of a still image as previous state-of-the-art works. This advantage allows it to provide a comprehensive diagnosis of both eardrum and ear canal diseases.INDEX TERMS Computer-aided diagnosis, convolutional neural network, deep learning, ear diseases, LSTM, otolaryngology, transfer learning.