The development idea of the system comes from the remote nursing demand put forward by Wu Jingmin, a surgeon in National Taiwan University Hospital, and combines the technology developed by NTU Institute of Student Medicine and Electronic Information with the professional judgment of NTU doctors. Dai Haozhi, director of plastic surgery at NTU Hospital, pointed out that NTU Hospital performs hundreds of operations every day and receives medical care during hospitalization. However, after discharge, wound tracking can only rely on regular follow-up. On one occasion, a diabetic patient was delayed in seeking medical treatment because of insensitivity until the follow-up found that the wound had festered and exploded. AI-SWAS has the functions of tracking, detection, explanation and timely feedback, which can reduce the incidence of postoperative infection and necrosis.
AI can accurately locate and correct, and the success rate of automatic interpretation is as high as 90%
Xu Ruize, a doctoral candidate at the Institute of Biomedicine and Electronic Information at National Taiwan University, said that the AI-SWAS platform includes the development of APP between doctors and patients and the analysis process of different changes in the affected area. At present, 46 photos of patients' wounds are collected every day. Doctors choose 13 1 photo as the basis for judging the database, and use AI-like neural network to calculate and accurately locate and analyze the changes of patients' wounds. In addition, the photo editing software built in AI-SWAS can correct the brightness and color of wound photos uploaded by patients, reduce the factors of misjudgment, and successfully realize the achievement of treating shaved skin tattoos as wounds.
Dai Haozhi pointed out that the response is good in clinical use at present. The accuracy of AI -SWAS automatic interpretation is above 90%, and the error value is below 65438 00%. Different situations are divided by color, blue represents necrosis, yellow represents infection and suppuration, pink represents redness, and red represents abnormality and bleeding, which greatly shortens the time for medical staff to interpret manually.
The birth of AI-SWAS is beneficial to patient safety.
Yu Zhongren, vice president of National Taiwan University Hospital, pointed out that the development and application of science and technology will help improve the quality of medical care. Last year, the establishment of the Artificial Intelligence and Robotics Research Center of National Taiwan University was closely related to clinical needs and research and development practicality, and the birth of AI-SWAS was the best verification. When the patient's wound healing changes, AI-SWAS provides reference data and information like a digital assistant, so that the patient can get the second opinion of the same kind of experts, and at the same time establish communication channels with the attending physician, so that the patient's condition can be properly handled, and the medical staff can conveniently carry out remote nursing, saving the patient's round-trip and medical workload, which is helpful to improve the safety, speed and accuracy of medical treatment in Taiwan Province Province.
At present, only the Android test system supports the user interface of patient-side applications.
(Provided by Public Affairs Office of National Taiwan University Hospital).
Ai -SWAS belongs to the experimental stage, which is beneficial to improve the bed turnover rate.
Lai Feiyu, medical secretary of NTU Hospital, pointed out that AI-SWAS is only applicable to NTU Hospital and Android system at present, and it is estimated that it can be listed in Google Play Store within 1 2 months at the earliest, while the iOS platform is expected to wait for 4 to 6 months. At present, the hospital plans to fully integrate AI-SWAS into NTU hospital portal system. The application scope of the system extends to the nursing of various wounds (non-surgical wounds). It is expected that this AI-SWAS application will assist the nursing of patients in other counties and cities after returning to China, realize the initial setting of telemedicine and effectively improve the hospital bed turnover rate.
Subject: Artificial Intelligence, Artificial Intelligence -SWAS, Artificial Intelligence, Wound Care, Artificial Intelligence and Robotics Research Center of National Taiwan University, National Taiwan University Hospital, Taiwan Province Provincial University.