Description of a Novel Web-Based Liposuction System to Estimate Fat Volume and Distribution
Bibliographic record
Abstract
BACKGROUND: Preoperative planning for liposuction is vital to ensure safe practice and patient satisfaction. However, current standards of fat assessment before surgery are guided by subjective methods such as visual inspection, skin-pinch tests, and waist circumference measurements. OBJECTIVES: This study aimed to develop an inexpensive software-based tool that utilizes ultrasound (US) imaging and an online platform to accurately simulate regional subcutaneous adipose tissue (SAT) distribution and safe volume estimation for liposuction procedures. METHODS: The authors present a web-based platform with integrated 2-dimensional (2D) and 3-dimensional (3D) simulations of SAT to support liposuction planning and execution. SAT-Map was constructed using multiple sub-applications linked with the python framework programming language (Wilmington, DE). RESULTS: The SAT-Map interface provides an intuitive and fluid means of generating patient-specific models and volumetric data. To further accommodate this, an operational manual was prepared to achieve consistent visualization and examination of estimated SAT content. The system currently supports static 2D heatmap simulation and 3D interactive virtual modelling of the SAT distribution. Supplementary clinical studies are needed to evaluate SAT-Map's clinical performance and practicality. CONCLUSIONS: SAT-Map revolutionizes the concept of preoperative planning for liposuction by developing the first combined web-based software that objectively simulates fat distribution and measures safe liposuction volume. Our software approach presents a cost-efficient, accessible, and user-friendly system offering multiple advantages over current SAT assessment modalities. The immediacy of clinically accurate 3D virtual simulation provides objective support to surgeons towards improving patient conversation, outcomes, and satisfaction in liposuction procedures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".