Inteligencia artificial en otorrinolaringología: estado actual y perspectivas a futuro / Artificial intelligence in otolaryngology: current status and future perspectives

La 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.

Artificial intelligence has a long history full of innovations that have resulted in different high-performance diagnostic resources currently available. This work has reviewed the artificial intelligence definition and its applications to medicine. We focused our review on otolaryngology's specialty to inform the medical community of the importance and the most relevant applications in the different diagnostic processes. We include an analysis of the current state of artificial intelligence in otolaryngology in Chile, and the challenges to be faced in the future to use artificial intelligence into daily medical practice.

Clinical Evaluation Criteria for Automatic Diagnosis of 12-lead Electrocardiograph / 中国医疗器械杂志

The automatic diagnosis function of the electrocardiograph (ECG) machine directly affects the clinical application of the device. However, there is currently no unified criteria for the evaluation of the automatic diagnosis function of the 12-lead ECG machine in clinic. We established a 12-lead ECG automatic diagnostic function clinical evaluation specification from the scope of the specification, the automatic diagnostic function judgment rules, general technical measurement requirements, test methods and conditions, and ECG data input. Emphasis is given to the judgment rules of the automatic diagnosis function, including the accuracy of ECG feature values, the interpretation and judgment of automatic diagnosis results. This specification aims to provide technical basis for the clinical evaluation for automatic diagnosis function of the 12-lead ECG.

Establishment of Application Database for Electrocardiograph in Different Scenes / 中国医疗器械杂志

It is significant to establish scene ECG database which improves the automatic diagnostic function of electrocardiograph under different application scenarios. We built the ECG database in different scene according to the hospital level (grade 3, grade 2, grade 1) and clinical environment (intensive care and acute wards, outpatient clinics and general wards). Sample size was obtained according to the incidence of various ECG diagnoses. The database covers ECG signal, ECG waveform, ECG characteristic values, ECG diagnostic results by experts and clinical information of patients etc. It not only provides important reference for electrocardiograph manufacturers to evaluate and test the parameters of automatic diagnosis under different clinical scene, but also provides valuable scientific research and teaching resources for medical workers.

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule / Software para predição diagnóstica automática de imagens clínicas da pele baseada na regra ABCD

Cancer is responsible for about 7 million annual deaths worldwide. Among them, the melanoma type, responsible for 4% of the skin cancers, whose incidence has doubled in the last ten years. The processing of digital images has shown good potential for assistance in the early detection of melanomas. In this sense, the objective of the current study was to develop a software for clinical images processing and reach a score of accuracy higher than 95%. The ABCD rule was used as a guide for the development of computational analysis methods. MATLAB was used as programming environment for the development of the processing of digital images software. The images used were acquired from two banks of free images. They included images of melanomas (n=15) and nevi images (not cancer) (n=15). Images in RGB color channel were used, which were converted to grayscale, 8x8 median filter applications and 3x3 neighborhood approach technique. After, we proceeded to the binarization and inversion of black and white for later extraction of contour characteristics of the lesion. The classifier used was an artificial neural network of radial basis, getting accuracy for diagnosis of melanomas images of 100% and of 90.9% for not cancer images. Thus, global correction for diagnostic prediction was 95.5%. An area under the ROC graph 0.967 was achieved, suggesting a great diagnostic predictive ability. Besides, the software presents low cost use, since it can be run on most operating systems used nowadays.

O câncer é responsável por cerca de 7 milhões de óbitos anuais em todo o mundo. Entre eles, o tipo melanoma, responsável por 4% dos cânceres de pele, cuja incidência dobrou mundialmente nos últimos dez anos. O processamento digital de imagens tem mostrado um bom potencial para auxiliar no diagnóstico precoce de melanomas. Neste sentido, objetivo do presente estudo foi desenvolver um software para processamento digital de imagens clínicas para diagnóstico automático baseado na regra ABCD que alcançasse um percentual de acerto maior do que 95% dos casos. Utilizou-se como norteador a regra ABCD para o desenvolvimento de métodos de análise computacional. Empregou-se o MATLAB como ambiente de programação para o desenvolvimento de um software para o processamento digital de imagens. As imagens utilizadas foram adquiridas de dois bancos de imagens de acesso livre. Foram inclusas imagens clínicas de melanomas (n=15) e imagens de nevos (lesão melanocítica benigna) (n=15). Utilizaram-se imagens no canal de cor RGB, as quais foram convertidas para escala de cinza, aplicação de filtro de mediana 8x8 e técnica de aproximação por vizinhança 3x3. Após, procedeu-se a binarização e inversão de preto e branco para posterior extração das características do contorno da lesão. O classificador utilizado foi uma rede neural artificial de base radial, obtendo acerto diagnóstico para as imagens melanomas de 100% e para imagens benignas de 90,9%. Desta forma, o acerto global para predição diagnóstica foi de 95,5%. Obteve-se uma área sob a curva ROC de 0,967, o que sugere uma excelente capacidade de predição diagnóstica, sobretudo, com baixo custo de utilização, visto que o software pode ser executado na grande maioria dos sistemas operacionais hoje utilizados.