The algorithm was developed using 5,545 images (65.5 percent containing polyps and 34.5 percent without polyps) from the colonoscopy reports of 1,290 patients. Experienced endoscopists annotated the presence of polyps in all images used in the development dataset, and the algorithm was then validated on four independent datasets: two sets for image analysis (A and B) and two sets for video analysis (C and D).
Key findings from the study include:
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Validation on dataset A, which included 27,113 images from patients undergoing colonoscopy at the Endoscopy Center of Sichuan Provincial People’s Hospital, found a per-image-sensitivity of 94.4 percent and a per-image-specificity of 95.9 percent.
The per-image-sensitivity in a subset of 1,280 images with polyps that are typically hard to detect was 91.7 percent.
Validation on dataset B, based on a public database of 612 colonoscopy images acquired from the Hospital Clinic of Barcelona, found a per-image-sensitivity of 88.2 percent. The use of this dataset allowed for generalization of the validation data to a broader patient population.
Validation on dataset C included a series of colonoscopy videos containing 138 polyps, found a per-image sensitivity of 91.6 percent among 60,914 frames of video, and a per-polyp sensitivity of 100 percent.
Validation on dataset D, which contained 54 colonoscopy videos without any polyps, found a per-image-specificity of 95.4 percent among 1,072,483 frames.
The total processing time for each image frame was 76.8 milliseconds, including preprocessing and displaying times before and after execution of the deep-learning algorithm. Implementation in a real-time system resulted in a processing rate of 30 frames per second with Nvidia Titan X GPUs.
The authors conclude that this automatic polyp-detection system based on deep learning has high overall performance in both colonoscopy images and real-time videos.
“Wision AI is committed to realizing the clinical value of AI and mathematical medicine in a variety of indications, including gastroenterology, ophthalmology, neurology, and radiation-based imaging,” said JingJia Liu, Chief Executive Officer at Wision AI. “The results of this study demonstrate the power of our rigorous approach to developing deep-learning algorithms, which utilizes distinct datasets for training and validation and results in high levels of specificity and sensitivity that have the potential to improve diagnostic screening methods that are known to reduce disease risk, improve health outcomes and save lives.”
