Examining the development, effectiveness, and limitations of computer-aided diagnosis systems for retained surgical items detection: a systematic review
Bibliographic record
Abstract
Retained surgical items (RSIs) can lead to severe complications, and infections, with morbidity rates up to 84.32%. Computer-aided detection (CAD) systems offer potential advancement in enhancing the detection of RSIs. This systematic review aims to summarise the characteristics of CAD systems developed for the detection of RSIs, evaluate their development, effectiveness, and limitations, and propose opportunities for enhancement. The systematic review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. Studies that have developed and evaluated CAD systems for identifying RSIs were eligible for inclusion. Five electronic databases were searched from inception to March 2023 and eleven studies were found eligible. The sensitivity of CAD systems ranges from 0.61 to 1 and specificity varied between 0.73 and 1. Most studies utilised synthesised RSI radiographs for developing CAD systems which raises generalisability concerns. Moreover, deep learning-based CAD systems did not incorporate explainable artificial intelligence techniques to ensure decision transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".