ArticleAesthetic plastic surgery2024
A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients.
Article in Aesthetic plastic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.Plastic and reconstructive surgery. Global open · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.Plastic and reconstructive surgery. Global open · 2026Article
- Exploring the Balance Between Artificial Intelligence and Human Expertise in Shaping Breast Reconstruction Outcomes: A Comparative Reflection Study.Journal of clinical medicine · 2026Article
- Design and evaluation of a remote damage control surgery real-time guidance system based on HoloLens 2 in low-speed network environments.Scientific reports · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Is My Doctor Human? Acceptance of AI among Patients with Breast Cancer.Plastic and reconstructive surgery. Global open · 2024Article
- Performance of ChatGPT in Answering Clinical Questions on the Practical Guideline of Blepharoptosis.Aesthetic plastic surgery · 2024Article
- Ethics for AI in Plastic Surgery: Guidelines and Review.Aesthetic plastic surgery · 2024Review
- BREAST AI-PLAN: Prompt-Driven AI Assistance for Breast Surgery Planning - A Retrospective Single-center Study.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc. MATERIALS AND
methodsWe combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis.
resultsOur model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans.
conclusionsOur AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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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.