Polyp characterization using deep learning and a publicly accessible polyp video database.

OBJECTIVES: Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or nonadenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database. METHODS: We trained a CNN with 16,832 high and moderate quality frames from 229 polyp videos (56 SSLs). It was evaluated with 222 polyp videos (36 SSLs) across two test-sets. Test-set I consists of 14,320 frames (157 polyps, 111 diminutive). Test-set II, which is publicly accessible, 3317 video frames (65 polyps, 41 diminutive), which was benchmarked with three expert and three nonexpert endoscopists. RESULTS: Sensitivity for adenoma characterization was 91.6% in test-set I and 89.7% in test-set II. Specificity was 91.9% and 88.5%. Sensitivity for diminutive polyps was 89.9% and 87.5%; specificity 90.5% and 88.2%. In NBI-NF, sensitivity was 89.4% and 89.5%, with a specificity of 94.7% and 83.3%. In NBI, sensitivity was 85.3% and 91.7%, with a specificity of 87.5% and 90.0%, respectively. The CNN achieved preservation and incorporation of valuable endoscopic innovations (PIVI)-1 and PIVI-2 thresholds for each test-set. In the benchmarking of test-set II, the CNN was significantly more accurate than nonexperts (13.8% difference [95% confidence interval 3.2-23.6], P = 0.01) with no significant difference with experts. CONCLUSIONS: A single CNN can differentiate adenomas from SSLs and hyperplastic polyps in both NBI and NBI-NF. A publicly accessible NBI polyp video database was created and benchmarked.

Medical applicant general practice experience and career aspirations: a questionnaire study.

BACKGROUND: Increasing access to general practice work experience placements for school students is a strategy for improving general practice recruitment, despite limited evidence and concerns surrounding equity of access to general practice experiences. AIMS: To examine the association between undertaking general practice experience and the perceptions of general practice as an appealing future career among prospective medical applicants. To identify socioeconomic factors associated with obtaining general practice experience. DESIGN & SETTING: Cross-sectional questionnaire study in the UK. METHOD: Participants were UK residents aged ≥16 years and seriously considering applying to study medicine in 2019/2020. They were invited to take part via the University Clinical Aptitude Test (UCAT). Questionnaire data were analysed using a linear regression of general practice appeal on general practice experience, adjusting for career motivations and demographics, and a logistic regression of general practice experience on measures of social capital and demographics. RESULTS: Of 6391 responders, 4031 were in their last year of school. General practice experience predicted general practice appeal after adjusting for career motivation and demographics (b = 0.37, standard error [SE] = 0.06, P<0.00001). General practice experience was more common among students at private (odds ratio [OR] = 1.65, 95% confidence interval [CI] = 1.31 to 2.08, P<0.0001) or grammar schools (OR = 1.33, 95% CI = 1.02 to 1.72, P = 0.03) and in the highest socioeconomic group (OR = 1.62, 95% CI = 1.28 to 2.05, P<0.0001), and less likely among students of 'other' ethnicity (OR = 0.37, 95% CI = 0.20 to 0.67, P = 0.0011). CONCLUSION: Having general practice experience prior to medical school was associated with finding general practice appealing, which supports its utility in recruitment. Applicants from more deprived backgrounds were less likely to have had a general practice experience, possibly through lack of accessible opportunities.