Track 8 Application of Computer Engineering in Virus Detection and Clinical Diagnosis and Treatment(ACEVDCDT) Track

Track Chair: Xuewen Qin, The Second Affiliated Hospital of Chifeng University (Cancer Research Institute of Chifeng University), Laboratory Administrative Director, Full professor. Email:
Track Co-chair:Lizhong Guo, The Second Affiliated Hospital of Chifeng University (Cancer Research Institute of Chifeng University), Deputy Administrative Director of Spine Surgery, Attending Physician, Email:
Track Co-chair:Shuran Huo, The Second Affiliated Hospital of Chifeng University (Cancer Research Institute of Chifeng University), Deputy director of laboratory administration, deputy chief inspector, Email:
Track Co-chair:Xiandan Chen, Belarusian state University, Doctor of Biomedical Science
Track Co-chair:Liangyu Li, The Second Affiliated Hospital of Chifeng University (Cancer Research Institute of Chifeng University), Distinguished Engineer,Email:
Track Co-chair:Yi Qin, The Second Affiliated Hospital of Chifeng University (Cancer Research Institute of Chifeng University), Otolaryngology Head and Neck Surgery Resident

Abstract--Computer engineering technology plays an important role in virus detection and clinical diagnosis. In future research, artificial intelligence will gradually replace some of the work of medical staff, especially in specimen collection, specimen experiment, disease diagnosis and clinical treatment. This branch believes that the detection of transmissible respiratory viruses is still worth studying, Epidemiologically, the outbreak of respiratory infectious virus is unavoidable, so how to use artificial intelligence to detect viral substances has become a key research field. In the diagnosis of clinical diseases, deep neural networks and expert systems have many application models in the clinical field. From a clinical perspective, Modeling and innovative research on the intelligent medical system required by hospital staff has become a very necessary research content. Under the leadership of Chairman Qin Xuewen, this branch will mainly engage in AI research on virus detection and clinical diagnosis and treatment using AI.The main concern is the rapid non manual testing of respiratory viruses. In addition, in the direction of respiratory virus intensive care, the branch also tries to exchange nursing staff and medical robots, reduce the workload of nursing staff, and solve practical problems.

Content: The main contributions received and research contents of the branch are divided into three points. The first point is how to design intelligent testing equipment for respiratory viruses in clinical laboratory work, how to connect the main physical structure of the sample transport equipment that conforms to the sterile principle with the chemical equipment by computer method, and how to solve the calculation problem of the overall clinical virus test report from the perspective of mathematical modeling, The design method of specific formulas and steps needs to be studied. At the same time, if an expert system is designed to realize unmanned respiratory virus detection, how can specific mechanical engineering equipment complete its work through computer technology. Second, through in-depth learning, such as computational immunology, computational genetics, and computational neuroscience, we can complete the automatic diagnosis model of diseases. At present, there are a large number of young doctors in Chifeng Cancer Hospital where the branch is located and around the world, who rely heavily on doctors with rich clinical experience in diagnosis. Obviously, the number of clinical patients also restricts the growth of young doctors. Then, through in-depth learning, we can complete the design of intelligent diagnosis software, It can meet the needs of these young doctors, and can avoid the occurrence of medical accidents caused by the wrong diagnosis of young doctors due to insufficient patients. Third, clinical departments such as orthopedics surgery and otorhinolaryngology have heavy surgical tasks, which have a serious impact on the health of the surgeon in charge. The surgeons in our branch suffer from occupational diseases of different degrees. This phenomenon is widespread in medical institutions. Therefore, it is very important to design a surgical robot that can assist the operation according to the artificial intelligence system, through fuzzy control and neural network algorithm, It also needs relevant research in teaching, nursing and other fields.

Invited Speakers

Professor Jiang Jun, Dean of School of Medical Humanities and Management, Hunan Medical University (tentative), Chifeng City Hospital affiliated to Inner Mongolia Medical University, neurosurgery department, medical doctor of Dusseldorf University, and clinical doctor Du Renfei (tentative)


We seek original completed and unpublished work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:

8Medical Imaging: Application of Deep Learning in Computer Imaging and Clinical Diagnosis

8Laboratory: through automatic virus and bacteria traceability of artificial intelligence, intelligent diagnosis software design is conducted through mathematical modeling, sampling and analysis of respiratory viruses are completed through computer engineering, and artificial intelligence robot modeling of unmanned medical institution laboratory.

8Spine surgery: complete orthopedic surgery through robots, perform open fracture emergency treatment through medical robots, complete ultra remote treatment of patients through remotely controlled Da Vinci surgical robots supported by 6G networks, and model knee replacement automation robots from the perspective of the fourth industrial revolution

8Otorhinolaryngology: a remote consultation platform for otorhinolaryngology surgery

8Gastroenterology department: automatic electronic fiber colonoscope robot modeling, and home screening capsule endoscopy modeling

8Thoracic surgery and cardiac surgery: mathematical modeling of intelligent thoracic puncture robot to judge the postoperative quality of life of patients undergoing thoracic surgery through in-depth learning

8Modeling of medical communication platforms between Chinese medical institutions and Russia, Belarus, and application of Chinese and Russian voice translation software in medical learning

8From the perspective of array economic product R&D: health economic product R&D based on AI technology

8Not limited to the above topics, all kinds of papers combining medicine and computer are welcome to submit