ArticleBone & joint open2026
Deep learning Algorithm for Wound assessment after total kNee (DAWN) arthroplasty : a prospective study protocol.
Article in Bone & joint open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: This study aims to develop and internally validate an AI approach based on a deep learning (DL) algorithm to classify wound photographs as either 'healing well' or 'requiring review' after a total knee arthroplasty (TKA). Methods: A prospective cohort study will be conducted at a single, high-volume Elective Orthopaedic Centre. Adult patients who have undergone primary TKAs will be recruited either upon re-attendance at a wound review clinic or if they have a wound concern. Within the first two weeks postoperatively, an orthopaedic research fellow will obtain two standardized photographs of the wound, and the participant will complete a six-item symptom survey. Two blinded consultant orthopaedic surgeons will independently label each case (with the photographic and survey knowledge only) as 'healing well' or 'requires review'. The dataset of wound images will be split into an 80:20 ratio and a pre-trained DL algorithm will be fine-tuned using 80% of the data with five-fold cross-validation being employed. The folds will be generated using stratification not only by outcome labels but also by demographic variables in order to maintain similar distributions across folds. The remaining 20% will be the test set allowing for internal validation and assessment of the efficacy of the developed algorithm. Ethical approval has been granted by the NHS REC/HRA (IRAS 340642). Conclusion: This study will generate one of the first prospectively validated DL tools for orthopaedic wound triage. Embedding objective imaging and symptom data into a DL algorithm will allow for early detection of complications, timely intervention, streamlined follow-up, and support NHS digital-first pathways. This study's design directly mirrors NHS post-TKA pathways, supporting translatability into the current postoperative workflow for patients. The development of an early-detection system further enables patients to communicate concerns and receive timely assessment and treatment of any postoperative wound issues. The findings of this study will be disseminated through peer-reviewed publications and presentations at national and international conferences.
Identifiers
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Registered trials
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.