The prosperity of artificial intelligence has become one of the new driving forces to promote social and economic development. It plays an important role in improving social production efficiency and realizing social development and economic transformation. As the core force leading the new generation of industrial transformation, artificial intelligence has demonstrated new applications in the medical field and has spawned new business formats through deep integration.
In fact, compared with manufacturing, media, retail, education and other fields, artificial intelligence is still in the early stage of medical care, with a relatively low degree of commercialization and a low industry penetration rate.
This is inevitable with the nursing and conservative nature of the medical industry. closely related. However, it is undeniable that the integration of artificial intelligence in the medical field has responded to many difficulties in traditional medical care, with extensive market demand, diverse business trends, and broad development space.
The COVID-19 pandemic promotes artificial intelligence from the cloud to play a key role and improve the overall anti-epidemic efficiency. The epidemic has become the touchstone of artificial intelligence in the medical field, showing the strength and value of artificial intelligence in medical treatment. From the perspective of application scenarios, artificial intelligence medical applications are still in their infancy, with image recognition, remote query, and health management temporarily occupying the first echelon.
Among them, image recognition , as a subdivision field of auxiliary diagnosis, is the most widely used scene of artificial intelligence in the medical field.
The concept of imaging diagnosis and treatment originated in the field of oncology, and then expanded to the entire field of medical imaging. Understanding medical imaging and extracting key information with diagnostic and treatment decision-making value is a very important link in the diagnosis and treatment process.
In the past, medical image preprocessing and diagnosis required the participation of 4-5 doctors. However, based on artificial intelligence image diagnosis and training computers to analyze medical images, only one doctor is involved in quality control and confirmation, which is of great benefit to improving the efficiency of medical behavior.
Artificial intelligence first exploded and landed in medical images, mainly because the access and processing of image data were relatively easy. Compared with medical records and other data accumulated for more than three to five years, the image data can be obtained in a few seconds with only one shot. An imaging film can reflect most of the patient’s condition and become the direct basis for the doctor to determine the treatment plan.
The huge and relatively standardized database of medical images and the continuous advancement of intelligent image recognition algorithms provide a solid foundation for the application of artificial intelligence medicine in this field.
The combination of artificial intelligence based on image recognition and deep learning and medical images can solve at least three needs.
1) First, focus recognition and labeling, that is, medical image segmentation, feature extraction, quantitative analysis, comparative analysis, etc. through the Al of medical image products. To meet this demand, the automatic identification, marking system of X-ray, CT, MRI and other medical images can greatly improve the diagnostic efficiency of imaging doctors. At present, the Al medical imaging system can quickly complete the processing of more than 100,000 images in a few seconds, improving the diagnosis accuracy, especially reducing the false negative probability of the diagnosis result.
2) Second, automatic delineation of the target area and adaptive radiotherapy. Target automatic drawing and self-drawing adaptive radiotherapy products can help radiotherapists automatically draw 200 to 450 CT films, which is greatly shortened to 30 minutes. And during the 15-20 times of the patient’s on-camera irradiation, the location of the lesion is continuously identified to achieve adaptive radiotherapy, which can effectively reduce the radiation damage to the patient’s healthy tissues.
3) Third, three-dimensional image reconstruction. The registration algorithm based on gray-scale statistics and the registration algorithm based on feature points can solve the problem of faulty image registration, save registration time, and play a role in lesion location, lesion range, benign and malignant lesion recognition, and surgical plan design.
From a technical point of view, medical image diagnosis mainly relies on image recognition and deep learning. According to the clinical diagnosis path, firstly, image recognition technology is applied to the perception link, unstructured image data is analyzed and processed, and useful information is extracted.
Secondly, use deep learning technology to input a large amount of clinical imaging data and diagnostic experience into the artificial intelligence model, so that the neuron network can be trained in deep learning. Finally, based on the algorithm model of continuous verification and grinding, intelligent reasoning of image diagnosis is carried out. Output personalized diagnosis and treatment judgment results.
From the perspective of the landing direction, the current layout of AI medical imaging products in most of the country is mainly concentrated in the chest, head, pelvis, limb joints and other major parts, mainly in the leading cities for cancer and chronic disease screening.
In the early days of the development and application of artificial intelligence medical imaging, lung nodule and fundus screening were popular areas. As the technology has matured and iterated in the past two years, major Al medical imaging companies are expanding their business scope. Breast cancer, stroke and bone age testing around bones and joints have become key areas for market participants. Aluminum medical imaging participates in the quantitative analysis and evaluation of the curative effect of new coronary pneumonia and has become a key force in improving the efficiency and quality of diagnosis.
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