Artificial Intelligence (AI) is revolutionizing the healthcare sector and it’s impact is already having a positive influence on cancer diagnosis and histopathology.
Histopathology is the study of cancer diagnosis through tissue samples. This microscopic examination of tissue samples is extremely intricate and tedious to perform even for an experienced lab technician. As a result, human error or fatigue can occur which may result in a misdiagnosis – an outcome all technicians want to avoid. The use of AI, however, can help prevent these errors by processing more data than humans in a shorter amount of time without the potential for fatigue, providing faster and more accurate results for cancer diagnosis.
Currently, AI tools are available that enable computation analysis of digital pathology slides, significantly reducing the workload for laboratory technicians and physicians. For instance, AI algorithms have been used to precisely identify and classify tumor cells from healthy tissue sample images with high accuracy rates rivaling or surpassing those of experienced pathologists themselves. This same technology could be employed in identifying metastasis, determining prognosis, and predicting therapeutic efficacy as well as making recommendations for personalized treatment plans for each patient based on their individual biopsies.
In addition to its usage in genome sequencing and gene expression profiling, AI’s most recent application is segmentation within histopathology images which is deployed with Convolutional Neural Network (CNN). This technique helps medical professionals identify microstructures at the sub-cellular level by recognizing patterns within the cell layers and tumor morphology. This increases accuracy levels immensely as it helps highlight suspicious regions in the image that would otherwise not be visible to conventional means. Furthermore, it leads to new possibilities such as malignancy prediction where AI can classify an image based on previously known malignant tissues’ sequences so that it can quickly make a conclusion – helping expedite delivery of care while minimizing scenarios associated with incorrect diagnoses/treatments.
AI is profoundly changing histopathology diagnostics with respect tumor detections and grading; its capabilities for imaging techniques like picture archiving communications systems (PACS); multi-parametric tests which have enabled clinicians interpret data using real-time information acquired from different machines; the interpretation of complex datasets involving millions of image profiles; along with identifying new markers for early warning signs related fatal diseases including cancers before they occur, resulting in better outcomes for patients and in research.
To learn more about AI in the lab, be sure to check out the upcoming webinar, AI in the Histology Lab, on March 22 at 1:00 PM (Eastern). In addition, the program for the 2023 NSH Convention, set to be released in April, will also include AI-related sessions.
Written by: Efrem Gebreyesus