AI Innovations To Help Tackle Cancer Screening Backlog

The diagnostics industry has been at the forefront of the battle against the global pandemic, with new technologies and solutions being developed more efficiently than ever before. The industry is now looking to artificial intelligence and digitisation to address the cancer screening backlogs that have built up during the pandemic.

Lifescience Industry reports that the innovation which was brought about in response to the global Covid outbreak will inform the approach to improving the detection, prevention, and treatment of breast and cervical cancer across the UK during the next few years.

There is currently a significant backlog, as breast and cervical cancer screening programmes were put on hold for several months. It is estimated that 600,000 cervical screening appointments were missed in April and May 2020. Furthermore, 986,000 mammograms were missed, meaning that up to 10,700 women may be living with undiagnosed cancer.

Testing is now resuming as the number of patients hospitalised with Covid is remaining low, despite the rise in cases. The article explains that new diagnostic procedures, particularly artificial intelligence guided imaging, are seen as a key tool to tackle delays in cancer diagnosis.

As cervical and breast cancer detection relies on visual detection of rare, subtle changes, it is ideally suited to AI. In this case, AI means the application of medical imaging to help accelerate detection and diagnosis. High quality images require advanced cloud storage solutions and high resolution.

The advanced technology will help to increase breast screening capacity, as fewer staff members will need to be involved in the diagnostic process. It is estimated that the process will be speeded up, as 13% less time is needed to read the mammogram images.

AI also has great potential to increase the efficiency of cervical cancer screening. The digital cytology systems are able to evaluate tens of thousands of cells from a single patient in a matter of seconds, and present the most relevant diagnostic material for analysis by a cytotechnologist. This has proved to increase efficiency without sacrificing accuracy.

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