Pathology is a complex medical field that plays a critical role in disease diagnosis and treatment. Traditionally, pathologists examine glass slides under microscopes to analyze tissue samples and detect diseases. This process can be time-consuming, tedious and prone to errors. Recent advancements in artificial intelligence (AI) and digital scanning technology are helping transform pathology into a digital field. Let’s explore how AI is impacting different areas of digital pathology.
Whole AI-based Digital Pathology
To digitize the AI-Based Digital Pathology process, tissue samples are first scanned using high-powered whole slide imaging (WSI) scanners. These scanners can produce digital reproductions of entire glass slides that rival or exceed the resolution of a conventional microscope. The digitized whole slide images (WSI) can then be analyzed on computer screens instead of microscope lenses. This offers several advantages like remote collaboration, storage of virtual slides for future reference, and automated analysis using AI algorithms. Leading WSI scanners today can produce high quality digital slides in under a minute.
AI-Powered Diagnosis
Pathologists often have to manually inspect slides under microscopes for hours to detect rare types of cancer cells or diagnose diseases. AI is helping address this bottleneck by automating parts of the diagnostic process. Deep learning algorithms are trained on huge datasets of digitized pathology slides where diseases have been previously diagnosed by pathologists. The models then learn to recognize visual patterns and features that indicate various diseases and cancers. Some AI systems today claim accuracy rates greater than 95% for detecting common cancers like breast and prostate on WSI images, helping prioritize cases for pathologists to review in detail. This could significantly boost diagnostic throughput in overburdened pathology labs.
Automated Screening
AI is also finding applications in screening of digitized slides at a massive scale. For example, in cancer screening programs, pathologists have to manually screen thousands of tissue slides every week to catch early signs of cancer. Automated screening using AI could dramatically speed this process up. Computer vision models equipped with a deep understanding of what normal and cancerous cells look like can rapidly scan whole slide scans and flag areas suspicious for disease. The pathologist then only needs to verify the findings instead of examining the whole slide. Some estimates suggest AI screening could increase pathology workflow by 2-10X, ensuring cancer screening programs have the necessary manpower.
Prognosis And Treatment
Beyond diagnosis, AI also shows promise in helping predict prognosis and tailor treatment plans for cancer patients. Looking at visual patterns in digitized tumor samples, deep learning models are able to uncover subtle morphological features associated with tumor aggressiveness, chances of spreading, and response to different therapies. This type of quantitative image-based analysis through AI could one day supplement existing methods like genomic testing to provide more accurate personalized prognostic information and treatment guidance to oncologists. Several startups are actively working on developing such AI-powered digital pathology tools for clinical use.
Data Analytics And Research
All the images, diagnoses and associated metadata generated from digital pathology workflows amount to a massive trove of potentially valuable data. AI is enabling new types of analytics on this data to advance research. For example, computational pathologists are using machine learning to uncover patterns in histology images that correlate with outcome, detect subtle image-based prognostic markers, discover new disease subtypes and predict drug responses. They can also generate hypotheses for further experimental validation. Continued application of AI to aggregate pathology datasets worldwide has the potential to revolutionize disease understanding and accelerate development of new diagnostics and therapeutics.
The digitization of pathology with whole slide imaging and introduction of AI show tremendous promise to modernize and scale up this important medical discipline. From accelerated diagnosis to more precise prognosis and treatment selection, computational pathologists assisted by AI have the ability to significantly improve patient care. Widespread adoption of AI-powered digital pathology tools will likely redefine workflows in anatomic pathology laboratories worldwide in the coming years.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
About Author – Vaagisha Singh
Vaagisha brings over three years of expertise as a content editor in the market research domain. Originally a creative writer, she discovered her passion for editing, combining her flair for writing with a meticulous eye for detail. Her ability to craft and refine compelling content makes her an invaluable asset in delivering polished and engaging write-ups. LinkedIn