ABSTRACT
In recent years, machine learning has become a hot spot in various research fields. Using machine learning can realize the transformation from data driven to knowledge discovery, which is an important development direction of laboratory intelligence in the future. The application of machine learning in laboratory medicine has shown great potential, but t it also has many challenges and difficulties. The direction of our joint efforts is to promote the clinical transformation of machine learning technology, realize the practicality and industrialization in medical laboratories, and achieve the goal of assisting clinical decision-making as soon as possible.
ABSTRACT
Antinuclear antibodies (ANA) testing is essential for the diagnosis, classification, and disease activity monitoring of systemic autoimmune rheumatic diseases. In recent years, with the enhancement of computing power and the innovation of algorithms, the newly hip branch, deep learning (DL), practically delivered all of the most stunning achievements and breakthroughs in artificial intelligence (AI) so far. The application of DL to visual tasks, known as computer vision, has revealed significant power within the medical image recognition. Indirect immunofluorescence on HEp-2 cells is the reference method for ANA testing, the results is interpreted manually by specialized physicians. ANA fluorescent pattern classification is based on image recognition, which has a broad prospect of combining with DL to realize automatic interpretation system. This paper reviews the recent research progress and challenges of DL in the field of ANA detection in order to provide references for the standardization of ANA testing in the future.
ABSTRACT
Laboratory testing is of great value in the management of autoimmune disease. The results can help confirm a diagnosis, estimate disease severity, aid in assessing treatment effect. But the current autoimmunity laboratory system, including testing standards, quality control and supervision, does not match the national conditions well. As a result, the test reports are not mutual-recognized among laboratories. In the current background of precision medicine, with the advances of technology and the application of deep learning and artificial intelligence in the clinical laboratory field, the autoimmune laboratory has ushered in a new development trend of integration, automation and intelligence.
ABSTRACT
Laboratory testing is of great value in the management of autoimmune disease. The results can help confirm a diagnosis, estimate disease severity, aid in assessing treatment effect. But the current autoimmunity laboratory system, including testing standards, quality control and supervision, does not match the national conditions well. As a result, the test reports are not mutual-recognized among laboratories. In the current background of precision medicine, with the advances of technology and the application of deep learning and artificial intelligence in the clinical laboratory field, the autoimmune laboratory has ushered in a new development trend of integration, automation and intelligence.