Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience.
When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of 6 human experts and 11% more likely to reduce false-positive exams. Humans and AI achieved similar results when radiologists were able to view prior CT scans.
Risk of cancer two after a screening the model was able to find cancer 9.5% more often but about 5% more likely to reduce false-positive exams compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study.
Detailed in research published today in Nature Medicine, the end-to-end deep learning model can predict whether a patient has lung cancer. It also generates a patient lung cancer malignancy risk score and identifies the location of the malignant tissue in the lungs.
The model will be made available through Google Cloud Healthcare API as Google continues trials and additional tests with partner organizations.
“The AI system uses 3D volumetric deep learning to analyze the full anatomy on chest CT scans as well as patches based on object detection techniques that identify regions with malignant lesions,”Google technical lead Shravya Shetty and product manager Daniel Tse said in a blog post today.
The model was trained using more than 42,000 chest CT screenings images. The CT scans were taken from near 15,000 patients, 578 of whom developed cancer within a year during the 2002 NLST study by the National Institutes of Health (NIH).
Results were then validated with datasets from Northwestern Medicine.
Lung cancer is one of the most common causes of death on Earth, according to World Health Organization data, taking more than 2 million lives annually, killing roughly as many people each year as breast cancer.
The NLST first carried out in 2002 by the National Institutes of Health (NIH) is a low-dose computed tomography (LDCT)screening. A 2015 analysis found that only 2-4% of patients get a LDCT screening today.
“By showing that deep learning can increase specificity without sacrificing sensitivity, we hope to spur more research and conversation around the role AI can play in tipping the cost-benefit scale for cancer screening,” the blog post reads.
The news follows the use of Google’s Inception v3 to detect lung cancer by New York University researchers last year
Deep learning is also behind Google’s advances in diabetic retinopathy diagnosis through eye scans, and DeepMind’s AI that can accurately recommend the proper line of treatment for 50 eye diseases with 94% accuracy.
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Khari Johnson, Khareem Sudlow
