2024
DOI: 10.1111/jgh.16491
|View full text |Cite
|
Sign up to set email alerts
|

Evaluating false‐positive detection in a computer‐aided detection system for colonoscopy

Taishi Okumura,
Kenichiro Imai,
Masashi Misawa
et al.

Abstract: Background and AimComputer‐aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false‐positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP.MethodsWe analyzed CADe‐assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre‐ and post‐update using 1:1 prop… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 40 publications
0
1
0
Order By: Relevance
“…False detection, false positives, and missed detection contribute significantly to false alarms in real-time monitoring systems. Achieving accurate detection relies on developing precise and generalized models capable of effectively identifying target objects [47]. Borowski et al addressed the challenge of high false-alarm rates within intensive care unit monitoring systems, primarily stemming from irrelevant noise and outliers in the time series of the sensor data.…”
Section: Reducing False Alarms During Real-time Monitoringmentioning
confidence: 99%
“…False detection, false positives, and missed detection contribute significantly to false alarms in real-time monitoring systems. Achieving accurate detection relies on developing precise and generalized models capable of effectively identifying target objects [47]. Borowski et al addressed the challenge of high false-alarm rates within intensive care unit monitoring systems, primarily stemming from irrelevant noise and outliers in the time series of the sensor data.…”
Section: Reducing False Alarms During Real-time Monitoringmentioning
confidence: 99%