Researchers have developed a new learning artificial intelligence (AI) system which can diagnose and identify cancerous prostate samples as accurately as any pathologist.
According to the researchers, this holds out the possibility of streamlining and eliminating variation in the process of cancer diagnosis. It may also help overcome any local shortage of trained pathologists.
“This is not going to replace a human pathologist. We still need an experienced pathologist to take responsibility for the final diagnosis,” said lead author Hongqian Guo from the Nanjing University in China.
“What it will do is help pathologists make better, faster diagnosis, as well as eliminating the day-to-day variation in judgement which can creep into human evaluations,” Guo added.
For the study, presented at the 33rd European Association of Urology Congress in Copenhagen, researchers took 918 prostate whole mount pathology section samples from 283 patients, and ran these through the analysis system, with the software gradually learning and improving diagnosis.
These pathology images were subdivided into 40,000 smaller samples; 30,000 of these samples were used to ‘train’ the software, the remaining 10,000 were used to test accuracy.
The results showed an accurate diagnosis in 99.38 percent of cases (using a human pathologist as a ‘gold standard’), which is effectively as accurate as the human pathologist.
They were also able to identify different Gleason Grades in the pathology sections using AI; ten whole mount prostate pathology sections have been tested so far, with similar Gleason Grade in the AI and human pathologist’s diagnosis.
The group has not started testing the system with human patients.
“The system was programmed to learn and gradually improve how it interpreted the samples. Our result shows that the diagnosis the AI reported was at a level comparable to that of a pathologist.
“Furthermore, it could accurately classify the malignant levels of prostate cancer,” Guo added.
Published Date: Mar 17, 2018 18:13 PM
| Updated Date: Mar 17, 2018 18:13 PM