Ibex, the First Ever AI-based Digital Pathology Cancer Diagnosis System
The Institute of Pathology at Maccabi Healthcare Services in Israel has deployed Ibex Medical Analytics’ Second Read (SR) System to identify various cell types and features within whole slide images of prostate core needle biopsies (PCNBs), including grading cancerous glands and other clinically significant features. Its use follows a pilot period in which it identified isolated major errors in retrospective PCNBs that were previously diagnosed as benign by pathologists.
“Prostate cancer diagnosis is labor intensive,” Joseph Mossel, co-founder and CEO of Ibex, the strategic partner of Maccabi, told HCB News. “It requires reviewing a large number of biopsy slides with potential for a human pathologist to miss relatively small cancer foci. Furthermore, there is potential for more mundane human error such as missing a slide within a case, typing errors and so forth. These risks have become more pronounced in recent years with an ever-increasing workload and a global shortage of pathologists.”
The SR system analyzes the entire case and alerts users to discrepancies found with the original diagnosis.
Following its deployment, the solution utilized AI and machine learning techniques to identify a suspicious PCNB that was diagnosed earlier in the day as benign by a pathologist at the institute, which handles approximately 700 PCNBs out of 160,000 histology accessions annually.
Staff re-examined the PCNB and confirmed the presence of low-grade prostate cancer, demonstrating the higher efficiency and accuracy of the platform.
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