MobileNetV3 network-based diagnosis of caries and periapical periodontitis from periapical films / 口腔疾病防治

Objective@#To research the effectiveness of deep learning techniques in intelligently diagnosing dental caries and periapical periodontitis and to explore the preliminary application value of deep learning in the diagnosis of oral diseases@*Methods@#A dataset containing 2 298 periapical films, including healthy teeth, dental caries, and periapical periodontitis, was used for the study. The dataset was randomly divided into 1 573 training images, 233 validation images, and 492 test images. By comparing various neural network models, the MobileNetV3 network model with better performance was selected for dental disease diagnosis, and the model was optimized by tuning the network hyperparameters. The accuracy, precision, recall, and F1 score were used to evaluate the model's ability to recognize dental caries and periapical periodontitis. Class activation map was used to visualization analyze the performance of the network model@*Results@#The algorithm achieved a relatively ideal intelligent diagnostic effect with precision, recall, and accuracy of 99.42%, 99.73%, and 99.60%, respectively, and the F1 score was 99.57% for classifying healthy teeth, dental caries, and periapical periodontitis. The visualization of the class activation maps also showed that the network model can accurately extract features of dental diseases.@*Conclusion@#The tooth lesion detection algorithm based on the MobileNetV3 network model can eliminate interference from image quality and human factors and has high diagnostic accuracy, which can meet the needs of dental medicine teaching and clinical applications.

Application and advance of artificial intelligence in biomedical field / 中国药科大学学报

@#Artificial intelligence (AI) has developed rapidly in the twentieth century, and has substantialy changed the modern way of life.At the same time, AI has greatly contributed to the development of the pharmaceutical industry, playing a key role in precision medicine, intelligent diagnosis, computer-aided drug design, and clinical trial decision-making, and has also greatly developed itself through its integration with the pharmaceutical industry.This paper outlines the key issues in research, describes the key applications of AI in the health and pharmaceutical industries, and finally analyzes the opportunities and challenges of AI in the health pharmaceutical industry to provide reference for the development of AI in the health and pharmaceutical fields.

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development / 医学前沿

Disorders of sex development (DSD) are a group of rare complex clinical syndromes with multiple etiologies. Distinguishing the various causes of DSD is quite difficult in clinical practice, even for senior general physicians because of the similar and atypical clinical manifestations of these conditions. In addition, DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD. Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses. On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence. "Chaining" inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information. Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis. The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-year residents. In conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSD-related diseases.

The Distributed Naive Bayesian Intelligent Diagnosis System Based on Hadoop / 医学信息学杂志

The paper introduces the research idea, design and realization of the distributed Naive Bayesian intelligent diagnosis sys-tem based on Hadoop, makes optimization and improvement according to its application in Traditional Chinese Medicine ( TCM) Hospital of Guangdong Province, including algorithm design improvement and enhancement of accuracy, extensibility and security of the system.

An Itelligent System for Diagnosis of Coronary Artery Disease with BP Neural Networks / 대한의료정보학회지

OBJECTIVE: In this paper, an intelligent system using BP neural networks (BPNN) is presented for early detection coronary artery disease (CAD). METHODS: Based on the four features of ECG signals and six basic parameters of patients, BPNN was built and trained. Especially the method which combined feature extraction and classification was discussed. RESULTS: The performance of the intelligent system has been evaluated in 20 samples. The test results showed that this system was effective in detecting CAD. The correct classification rate was about 90% for normal subjects and 100% for abnormal subjects. CONCLUSION: BPNN could quite accurately detect abnormal subjects. Because it is not expensive and noninvasive, it is fit to examine health of the elderly and has good application foreground.