Evidence map›Paper›PMID 42449022›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Computer assisted intervention to improve diagnostic yield in capsule endoscopy post ingestion.

Krispian Lawrence, Usha Goparaju, Luis Lamb

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Krispian LawrenceEquitable Technologies, Cambridge, MA, USA. krispian@mit.edu.
Usha GoparajuEquitable Technologies, Cambridge, MA, USA.
Luis LambCatholic Institute of Technology, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWireless capsule endoscopy (WCE) examinations frequently become non-diagnostic due to early visibility degradation caused by dirt, bubbles, or sustained mucosal occlusion. Existing computational approaches typically assess examination quality retrospectively, after completion of the study, limiting the opportunity for corrective intervention. We propose a feasibility-aware computer-assisted intervention (CAI) framework that reformulates WCE quality management as a sequential decision problem during acquisition.

methodsEarly checkpoints within the first 10-25% of each examination summarize frame-level visibility impairment using four structured components: mean impairment severity, temporal instability, worst-case continuous collapse, and dominant degradation mechanism cues. These early observations are integrated through logistic normalization to estimate downstream non-diagnostic risk without access to future frames. The framework further incorporates feasibility constraints through estimation of remaining diagnostically relevant transit time and intervention onset latency, enabling anatomically and temporally consistent action recommendations.

resultsOn the clinically annotated GALAR technical subset, early visibility dynamics from the first 20% of examinations achieved non-trivial discrimination of downstream non-diagnostic outcomes (AUROC

conclusionCapsule endoscopy quality management can be formally reframed as a sequential, feasibility-aware CAI problem. The proposed framework enables early, mechanism-consistent, and anatomically feasible intervention decisions rather than purely retrospective quality assessment.

Indexed as

Capsule EndoscopyImage Interpretation, Computer-AssistedFeasibility StudiesHumansCapsule endoscopyComputer-assisted interventionEarly failure predictionFeasibility-aware decision makingImage quality assessment

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.