Identification analysis and traceability application of food in key fields based on artificial intelligence

Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases

Current Medicinal Chemistry ◽

10.2174/0929867328666210405114938 ◽

2021 ◽

Vol 28 ◽

Author(s):

Rajnish Kumar ◽

Farhat Ullah Khan ◽

Anju Sharma ◽

Izzatdin BA Aziz ◽

Nitesh Kumar Poddar

Keyword(s):

Artificial Intelligence ◽

Risk Assessment ◽

Decision Support ◽

Randomized Clinical Trials ◽

Prediction Models ◽

Medical Sciences ◽

Pancreatic Diseases ◽

Substantial Progress ◽

Identification Analysis ◽

Wireless Capsule

: There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient’s treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.

Get full-text (via PubEx)

Diagnóstico en base a la NOM-036-1-STPS-2018 para evaluación de riesgo ergonómico en puestos operativos

Revista Relayn - Micro y Pequeñas empresas en Latinoamérica ◽

10.46990/relayn.2021.5.3.187 ◽

2021 ◽

Vol 5 (3) ◽

pp. 212-233

Author(s):

Adriana Fragoso Mora ◽

María Eugenia Sánchez Ramos ◽

Gerardo Pérez Duarte Marcoux

Keyword(s):

Artificial Intelligence ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Factores De Riesgo ◽

Technical Evaluation ◽

Para Determinar ◽

Identification Analysis ◽

And Control ◽

Ergonomic Risk Factors

La presente investigación tiene como objetivo para identificar, analizar y controlar los factores de riesgo ergonómico bajo la NOM-036-1-STPS-2018 en puestos operativos a partir de tecnologías de inteligencia artificial. Los materiales y métodos utilizados están con base los requerimientos técnicos aplicados mediante evaluación técnica a partir de un estudio observacional, descriptivo de corte transversal con procesamiento de datos en una arquitectura tecnológica basada en un modelo de estimación de pose humana que utiliza la Tecnología Tensorflow por medio de una red neuronal artificial convolucional para determinar los índices en posturas ergonómicas con riesgo significativo y posturas sanas para los puestos involucrados en el diagnóstico. AbstractThe objective of this research is the identification, analysis, and control of ergonomic risk factors in operational positions under NOM-036-1-STPS-2018 obtained by means of artificial intelligence technologies. The materials and methods used are based on the technological requirements applied, by means of a technical evaluation on basis of an observational, descriptive, cross-sectional study with data processing, in a technological architecture based on an estimative model of human posture using TensorFlow technology, through an artificial convolutional neuronal network to determine indexes of hazardous ergonomic posture with good posture for operational positions involved in this diagnosis.

Get full-text (via PubEx)