5 Key Advantages of adapting to Computer Vision Technology in Clinical Pathology

5 Key Advantages of adapting to Computer Vision Technology in Clinical Pathology

Clinical Pathology Using AI & ML – Overview

  • To assist medical professionals, Computer vision technology – a part of AI and ML, has been used in various healthcare applications – especially in the field of clinical pathology.
  • Adapting to AI&ML helps pathologists analyze the images of histology slides using image analysis and machine learning. 
  • Medical imaging or medical image analysis is one such method that creates a visualization of organs and tissues to enable a more accurate diagnosis.
  • Pathologists and laboratorians are therefore excited about the promise that AI/ML can bring to their ability to impact health care

Why AI in Healthcare Industry?

  • To understand the daily patterns and needs of the people, healthcare professionals adapt to AI technology.
  • With the adaption of AI & ML medical professionals can provide better feedback, guidance, and support for staying healthy.
  • AI in healthcare is the cognitive discipline for medical diagnosis purposes. Artificial intelligence can assist doctors, nurses, and other healthcare workers in their daily work.

Computer Vision in Clinical Pathology – 5 Key Advantages

  • Aiding And Automating the Analysis of Pathology Images
  • Automating The Analysis of Terabytes of Data
  • Eliminate The Chances of Human Error in Diagnosis
  • Accurate Diagnosis of Life-Threatening Ailments
  • CV Models Increase the Efficiency of Analysis

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How did we successfully implement a CV model & helped pathologists classify image tiles as benign (or) malignant?

Solution Overview

  • The main aim of the solution is to automate and accelerate the discovery of new drugs and treatment possibilities.
  • Another critical factor is to deal with histology images where the texture, spectral and structural features such as the nucleus are identified with the help of Image processing.
  • The intent is to train Computer Vision-based Deep Learning models that can classify pathology image tiles as benign or malignant.
  • The spectral features involve finding out the optical density format of the given images and obtaining the stain vectors and intensity for the stains involved in the histology images such as Haematoxylin, Eosin, and Residual.
  • This helps pathologists to automate the analysis of terabytes of data to eliminate the chances of human error.

Market Size: AI & ML in Healthcare Industry

Artificial intelligence in the diagnostics market size was valued at USD 576.3 million in 2021 and is projected to grow at a compound annual growth rate (CAGR) of 26.3% from 2022 to 2030.

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