Clin Endosc.  2024 Mar;57(2):217-225. 10.5946/ce.2023.145.

Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand

Affiliations
  • 1Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok
  • 2Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Abstract

Background/Aims
This study aims to compare polyp detection performance of “Deep-GI,” a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model.
Methods
Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen.
Results
In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%.
Conclusions
Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.

Keyword

Artificial intelligence; Colonoscopy; Detection; Polyps; Trainee

Figure

  • Fig. 1. Example of an adenomatous polyp detected by AI models. (A) A diminutive sessile polyp detected by the computer-aided polyp detection (CADe) model; (B) the same polyp detected by the Deep-GI model; (C) and (D) the alarm-tracing program detecting the bounding boxes of CADe and Deep-GI, respectively.

  • Fig. 2. An adenomatous polyp >1 cm (within the red box) that was missed by Deep-GI, computer-aided polyp detection (CADe), and the trainees.


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