Smart software can diagnose prostate cancer, says study
Beijing: Scientists in China have developed a learning artificial intelligence (AI) system which can diagnose and identify cancerous prostate samples as accurately as any pathologist.
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.
Prostate cancer is the most common male cancer, with around 1.1 million diagnoses ever year. Confirmation of the diagnosis normally requires a biopsy sample, which is then examined by a pathologist.
Now an artificial intelligence learning system, presented at the European Association of Urology Conference in Copenhagen, has shown similar levels of accuracy to a human pathologist.
In addition, the software can accurately classify the level of malignancy of the cancer, so eliminating the variability which can creep into human diagnosis.
"This is not going to replace a human pathologist. We still need an experienced pathologist to take responsibility for the final diagnosis. 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," said Hongqian Guo from Nanjing University in China.
The 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 per cent of cases (using a human pathologist as a gold standard), which is effectively as accurate as the human pathologist.
"The system was programmed to learn and gradually improve how it interpreted the samples. Our result show that the diagnosis the AI reported was at a level comparable to that of a pathologist," Guo said.
"Furthermore, it could accurately classify the malignant levels of prostate cancer," Guo added.
Mar 17, 2018
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