Use of Artificial Intelligence in Histopathology

Artificial intelligence and histopathology

Histopathology is the study of observing changes in tissues during a disease state. It is considered as the gold standard for disease diagnosis. Combining histopathology with artificial intelligence leads to better accuracy of this technique.

The process of identifying a pathology in a tissue involves manually observing the tissue slides under magnification. This process requires a well trained pathologist and is time consuming. The use of deep learning based AI solutions are beneficial here. It helps to study the images of the specimen in depth and make an accurate diagnosis. It also reduces the burden on the pathologists and helps with the workflow. A digital repository of these images can be created in the process which helps in sharing digital data and thus helps easy collaboration of pathologists with other specialists.

The use of artificial intelligence in the pathology is beneficial in reducing the chances of errors. It helps in an in depth study of image specimens and an accurate diagnosis.

The algorithms applied to histopathology can also pick up some distinguishing markers by itself. The information is passed through various levels of neural networks which extracts relevant information and provides valuable insights.

Use cases of AI in histopathology

⦁ Detection, segmentation and classification of tissues. ⦁ It can be applied to whole slide imaging to quickly identify and detect the pattern. ⦁ Beneficial for primary diagnostic work due to digital access of slides. ⦁ Detection of breast cancer metastasis in a lymph node. It has high success rates with a sensitivity of 75% and specificity of almost 90%. ⦁ Analyzing cell counts. eg Hematology ⦁ It can also help in image analysis, virtual education, balancing, and managing workload. ⦁ Assist in central clinical trial review and innovative research.

Challenges of use of AI in Histopathology

⦁ Lack of annotated data for training the algorithm ⦁ Digitization of images is an initial challenge as it is a highly time consuming process ⦁ Difficulty in defining the correct magnification for the analysis of tissues.