Application prospect of computer vision technology assisted ultrasonography in diagnosis and treatment of liver diseases / 中华消化外科杂志

Ultrasound examination has the advantages of non-radiation, non-invasive, low cost and high efficiency, and is the most commonly used method of liver imaging examination. In recent years, the application of computer vision technology to the intelligent analysis of ultrasound images has become a research hotspot in the field of intelligent healthcare. Through large-scale data training, the intelligent analysis model of ultrasound omics based on machine learning algorithm can assist clinical diagnosis and therapy, and improve the efficiency and accuracy of diagnosis. Based on the literature, the authors summarize the application proprect of computer vision technology assisted ultrasonography in the evaluation of diffuse liver lesions, focal liver lesions, microvascular invasion of liver cancer, postoperative recurrence of liver cancer, and postoperative therapy response to trans-catheter arterial chemoembolization.

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.

The application of computer vision in quality traceability management in CSSD / 中国医学装备

Objective:To make the quality of STF detection based on traceability management process realize automation, thereby reducing the error evaluation lead to artificial visual error, impartiality and improve the reliability of quality traceability system.Methods: Using image acquisition system, the introduction of computer vision technique in STF cleanliness testing process, so the evaluation results of cleanliness for STF automation, reduce the artificial dependence, improve the fairness and reliability.Results: compared with the previous manual evaluation method to inspect based, automatic generation of test results, more efficient, more accurate.Conclusion: Using the visual processing technology in STF inspection, can reduce the lead toartificial inspection subjective miscarriage of justice, fairness and improve the reliability of the detection conclusion.