ArticlePlastic and reconstructive surgery. Global open2021
Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery.
Article in Plastic and reconstructive surgery. Global open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning.Aesthetic plastic surgery · 2026Article
- A remote monitoring system based on deep learning for real-time assessment of free flaps.PloS one · 2026Article
- Machine learning approaches overcome imbalanced clinical data for intraoral free flap monitoring.Scientific reports · 2025Article
- Revolutionizing cleft lip and palate management through artificial intelligence: a scoping review.Oral and maxillofacial surgery · 2025Article
- Age Reduction After Face-Lift Surgery in Chinese Population: An Outcome Study Using Artificial Intelligence and Objective Observer-Based Assessment.Aesthetic plastic surgery · 2024Article
- Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.Scientific reports · 2024Article
- Autonomous Robotic Surgery: Has the Future Arrived?Cureus · 2024Review
- Bard Versus the 2022 American Society of Plastic Surgeons In-Service Examination: Performance on the Examination in Its Intern Year.Aesthetic surgery journal. Open forum · 2024Article
- A Narrative Review of Artificial Intelligence (AI) for Objective Assessment of Aesthetic Endpoints in Plastic Surgery.Aesthetic plastic surgery · 2023Review
- "Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm.Plastic surgery (Oakville, Ont.) · 2023Article
- Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review.Aesthetic plastic surgery · 2023Article
- A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models.The American surgeon · 2023Article
- Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.The American surgeon · 2023Article
- Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery.Plastic and reconstructive surgery. Global open · 2022Article
- Toward a Universal Measure of Facial Difference Using Two Novel Machine Learning Models.Plastic and reconstructive surgery. Global open · 2022Article
- Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.Emerging topics in life sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMachine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.
methodsEMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.
resultsThe database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).
conclusionsML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.
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.