ReviewKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2023
A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II.
Review in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 3D imaging-based AI models outperform demographic models and excel in tibial sizing compared with 2D models in total knee arthroplasty planning: A systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Pooled it
- Standardised Transparent Orthopaedic Reporting and Modelling for AI (STORM-AI)-Guidelines for reporting artificial intelligence studies in orthopaedics from the ESSKA AI Working Group.Journal of experimental orthopaedics · 2026Article
- Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Article
- Development of a novel artificial intelligence clinical decision support tool for hand surgery: HandRAG.Journal of hand and microsurgery · 2025Article
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Mapping the reporting practices in recent randomised controlled trials published inJournal of experimental orthopaedics · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Simulation of osteotomy in total knee arthroplasty with femoral extra-articular deformity assisted by artificial intelligence: a study based on three-dimensional models.Journal of orthopaedic surgery and research · 2024Article
- Harnessing deep learning and statistical shape modelling for three-dimensional evaluation of joint bony morphology.Journal of experimental orthopaedics · 2024Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.Journal of experimental orthopaedics · 2024Review
- Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.Arthroplasty today · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning-based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three-part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre-processing and augmenting the data, making use of open-source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to "shallow" machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.
Indexed as
Identifiers
36773057What OpenQuestion holds
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.