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28th September 2021
Prostate cancer is the second most common cancer in men, with 1.3 million new cases recorded in 2018. Confirmation of a prostate cancer diagnosis can only be achieved via biopsy and subsequent examination of digitalised slides of the biopsy. Now, the first artificial intelligence (AI) software for in vitro diagnostic detection cancer in prostate biopsies has been approved by the FDA in the US. The software is designed to identify an area of interest on the prostate biopsy image with the highest likelihood of harbouring cancer. This alerts the pathologist if the area of concern has not been noticed on their initial review and thus can assist them in their overall assessment of the biopsy slides.
The AI system approved is Paige Prostate and it is anticipated to increase the number of identified prostate biopsy samples with cancerous tissue and ultimately save lives. The FDA approval was based on a study of Paige Prostate undertaken with three pathologists. In the study, which was conducted in two phases, each pathologist was required to assess 232 anonymised whole slide images and asked to dichotomise these as either cancerous or benign, with only 93 slides (40%) that were in fact cancerous. In the first phase, the pathologists assessed the scans alone, whereas in the second phase, 4-weeks later, the same scans were reviewed but this time using the AI software, Paige Prostate.
In the study, the Paige Prostate software alone, had a sensitivity for detecting cancer of 96% and a specificity of 98%. Without the use of Paige Prostate, the pathologists averaged a sensitivity of 74% but with the addition of the AI software, their average sensitivity increased significantly to 90% (p < 0.001). Addition of Paige Prostate mainly improved pathologists’ detection of grade 1 to 3 cancers. However, despite a greater sensitivity from the use of Paige Prostate, there was no significant difference in specificity (p = 0.327) since this was already high at an average of 97% without Paige Prostate.
Source. FDA Press release September 2021
9th October 2020
Now a team from Weill Cornell Medicine, New York, has created an artificial intelligence (AI) system that can use routine test data results to determine if a patient has COVID-19. Normally, clinicians order a battery of blood tests in addition to a PCR test, including routine laboratory tests and a chest X-ray and these results are generally available within 1 – 2 hours. Researchers therefore hypothesised if the results of the routine laboratory test could be used to predict if someone was infected with COVID-19 without the PCR test. The included patient demographics such as age, sex, race into a machine learning model and incorporated the results for 27 routine tests. The laboratory results were made available two days before the PCR test result. The dataset included a total of 5893 patients admitted to hospital between March and April 2020 and they excluded individuals under 18 years of age and those who PCR result was inconclusive and patients without laboratory test results within two days prior to the PCR test.
A total of 3356 patients who were tested for COVID-19 were included with a mean age of 56 years of whom, 1402 were positive and 54% emergency department admissions. Using a machine learning technique known as a gradient boosting decision tree, overall, the algorithm identified COVID-19 positivity with a sensitivity of 76% and a specificity of 81%. However, limiting the analysis to emergency department patients, increased the sensitivity to 80% and the specificity to 83%. Moreover, the algorithm correctly identified those who had a negative COVID-19 test result. A recognised limitation was the testing was specific those admitted to hospital with moderate to severe disease and thus requires further work to identify milder cases.
Nevertheless, the authors concluded that their algorithm is potentially of value in identifying whether patients have COVID-19 before they receive the results of a PCR test.
Yang HS et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin Chem 2020; https://doi.org/10.1093/clinchem/hvaa200