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

Clinical Pathology Using AI & ML – Overview

  • Computer vision technology, which really is a subset of AI and ML, has been applied in a number of hospital systems to help medical practitioners, notably in the field of clinical pathology.
  • Pathologists can use image classification and machine learning to analyse the image of histology slide slides by conforming to AI&ML.
  • One such approach is health imaging, or medical image analysis, which portrays organs and tissues to help with diagnosis.
  • Therefore, pathologists and lab technicians are motivated by the potential that AI and ML have had for their potential to manipulate the provision of health care.

Why AI in Healthcare Industry?

  • Healthcare professionals adopt AI technology in order to comprehend the everyday routines and demands of the public.
  • Medical practitioners can now offer improved feedback, direction, and assistance for maintaining health thanks to the adaptation of AI and ML.
  • AI is the cognitive science used in healthcare for medical diagnosis. Doctors, nurses, and other healthcare professionals can benefit from using artificial intelligence in their regular 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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