Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases.

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.

Exploring Functional Differences between the Right and Left Ventricles to Better Understand Right Ventricular Dysfunction.

The right and left ventricles have traditionally been studied as individual entities. Furthermore, modifications found in diseased left ventricles are assumed to influence on right ventricle alterations, but the connection is poorly understood. In this review, we describe the differences between ventricles under physiological and pathological conditions. Understanding the mechanisms that differentiate both ventricles would facilitate a more effective use of therapeutics and broaden our knowledge of right ventricle (RV) dysfunction. RV failure is the strongest predictor of mortality in pulmonary arterial hypertension, but at present, there are no definitive therapies directly targeting RV failure. We further explore the current state of drugs and molecules that improve RV failure in experimental therapeutics and clinical trials to treat pulmonary arterial hypertension and provide evidence of their potential benefits in heart failure.