Person: Maass, Juan
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Publication Inteligencia artificial en otorrinolaringología: estado actual y perspectivas a futuro(2022) Talamilla P., Matías; Ignacio Vargas V., Ignacio; Cisternas P., Irma; Viscaíno S., Michelle; Auat-Cheein, Fernando; Délano R., Paul; Maass, JuanLa inteligencia artificial posee una larga historia, llena de innovaciones que han dado como resultado diferentes recursos diagnósticos de alto rendimiento, que se encuentran disponibles actualmente. En este artículo se presenta una revisión sobre la inteligencia artificial y sus aplicaciones en medicina. El trabajo se centra en la especialidad de otorrinolaringología con el objetivo de informar a la comunidad médica la importancia y las aplicaciones más destacadas en los diferentes procesos diagnósticos dentro de la especialidad. Incluimos una sección para el análisis del estado actual de la inteligencia artificial en otorrinolaringología en Chile, así como los desafíos a enfrentar a futuro para utilizar la inteligencia artificial en la práctica médica diaria.Publication Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases(2022) Viscaino, Michelle; Talamilla, Matias; Maass, Juan; Henríquez, Pablo; Délano, Paul; Auat, Cecilia; Auat, FernandoArtificial 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.