Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy

By Pu Wang, Xiao Xiao, Jeremy R. Glissen Brown, T. Berzin, Mengtian Tu, Fei Xiong, Xiao Hu, Pei-xi Liu, Yan Song, Di Zhang and 6 more. Nature Biomedical Engineering, 2018

434 citations recorded by the source corpus.

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists. A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.

Subfields: Oncology, Pulmonary and Respiratory Medicine, Surgery

View Paper: https://www.semanticscholar.org/paper/cc235fbdf72d0e64a8a496fba98b2a31591ce6ce

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Citations in the corpus

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (2020, neutral): “SegNet [32] - 0.”
ResUNet++: An Advanced Architecture for Medical Image Segmentation (2019, background): “The performance of the machine learning software has come close to the level of expert endoscopists [9]–[12].”
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On evaluation metrics for medical applications of artificial intelligence (2021, background): “In Study 4 , Wang et al. 19 report high per-image-sensitivity (recall) values of 0.94, 0.92 and 0.92, see Table 2 in 19 , or metric set 1 under study 4 in Table 1.”
Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study (2019, positive): “The aim of this study was to investigate whether a high-performance real-time automatic polyp detection system 18 can increase polyp and adenoma detection rates in the real clinical setting.”
Key challenges for delivering clinical impact with artificial intelligence (2019, background): “The limited number of prospective studies to date include diabetic retinopathy grading [48 – 50], detection of breast cancer metastases in sentinel lymph node biopsies [51, 52], wrist fracture detection [53], colonic polyp detection [28, 54], and detection of congenital cataracts [55].”