With the rapid development of technology as well as the continuous improvement of hardware equipment, the combination of artificial intelligence (AI) and medical treatment is becoming more and more widely applied. At present, the application scenarios of AI in the medical field mainly include medical imaging, intelligent diagnosis and treatment, smart guidance, intelligent voice, health management, case analysis, hospital management, new drug development and medical robots, etc.
Medical imaging provides the most basic reference material for doctors in diagnosis of diseases. Through the analysis and comparison of the images, the illness with the patient can often be specified. But in the actual process of medical imaging, doctors do come across problems.
(1) There is often a shortage of human resources for imaging diagnosis in hospitals. Medical institutions generally lack high-level imaging doctors. Moreover, imaging diagnosis is actually very complicated as there might be different images for the same disease or the same images for different disease.
(2) Doctors are generally good at qualitative analysis; however, traditional qualitative analysis is not 100% accurate, and sometimes diagnostic errors occur. Many small quantitative changes cannot be judged by the naked eye, and it is difficult to do quantitative analysis.
(3) The doctor often spends a longer time on image reading. The current image is mainly presented in the form of data and images, rather than the most effective information, which greatly limits the speed of doctors’ manual reading.
The “AI + medical imaging” model aids disease diagnosis
The introduction of AI into medical imaging can effectively solve some problems, especially in the following aspects:
1. Image reconstruction
Through algorithmic image mapping technology, AI can restore images when only a small amount of signals to an image is collected, and by using image reconstruction technology, high-dose quality images can be reconstructed from low-dose CT and PET images. This can reduce the risk of radiation while meeting the needs of clinical diagnosis.
2. AI diagnosis of diseases
AI can aid in the diagnosis of a wide range of diseases, such as lung disease, cardiovascular disease, brain disease, nervous system disease, etc.
(1) AI-assisted diagnosis of lung diseases
“AI + CT imaging” is used in the identification of lung nodules. AI can effectively and easily identify nodular nodules, such as solid nodules under 6mm, while providing nodule location, size, density and nature. The accuracy rate is about 90%. In addition, it can screen for lung diseases such as tuberculosis, pneumothorax, and lung cancer.
(2) AI-assisted diagnosis of brain diseases
Currently, AI can assist the diagnosis of brain diseases such as cerebral hemorrhage, internal atherosclerosis, intracranial aneurysms, and carotid vulnerable plaque. Among them, intracerebral hemorrhage is a refractory disease with a high fatality rate in neurosurgery. The “AI + CT” model, based on machine vision and deep learning technology, can quickly locate the area of cerebral hemorrhage, accurately quantify the volume of bleeding, determine whether there is cerebral hernia, and at the same time, can complete the professionally demanding and time-consuming image evaluation at the speed of seconds. This can help doctors make accurate judgments and allow patients to get the best treatment plan in the first time.
(3) AI-assisted diagnosis of nervous system diseases
The application of AI in neurological diseases mainly includes epilepsy, Alzheimer’s disease and Parkinson’s disease. AI can process and analyze the patient’s image data and make a statistical comparison with the normal group, so as to calculate the size and location of metabolic abnormalities. Through cognitive technology, it gives recommendations for treatment plans and treatment effects prediction.
(4) AI-assisted diagnosis of cardiovascular diseases
Based on chest CT scan data, AI can use deep learning technology and image processing technology to design specific algorithms to evaluate coronary artery vulnerable plaques, perform intelligent assisted diagnosis of coronary heart disease, and plan stent implantation plans. At the same time, it can intelligently diagnose complex diseases such as aortic disease types and aortic aneurysms.
3. Smart outline of target area
At present, radiotherapy is one of the main treatment methods for cancer patients, and the correct positioning and accurate delineation of diseased organs are the basis and key technology of radiotherapy. Therefore, before radiotherapy, it is necessary to mark the organs and tumor locations on the CT image. According to the traditional method, it usually takes 3 to 5 hours for the doctor.
The application of AI technology can greatly improve the efficiency. The high accuracy of AI intelligently delineating the target area can largely avoid the ineffective treatment caused by the inaccurate delineation of the target area. At present, the “AI + target area” has been successfully applied to lung cancer, breast cancer, nasopharyngeal cancer, liver cancer, prostate cancer, esophageal cancer and skin cancer.
4. Intelligent judgment of pathological sections
The judgment of pathological section is a complex task, which often requires doctors to have very rich professional knowledge and experience, and even those seasoned doctors can easily ignore the details that are not easy to detect and thus lead to deviations in diagnosis. The introduction of AI into the study of pathological and pathological slices, through studying the cell-level characteristics of pathological slices and constantly improving the knowledge system of pathological diagnosis, is the best way to enhance the reading efficiency and diagnostic accuracy.
Medical imaging has become the most popular application of artificial intelligence in the medical field. Despite that there are still certain challenges in the actual application, it is believed that “AI + medical imaging” will be quickly commercialized in the future with the continuous development of AI-related technologies and the continuous improvement of relevant policies.