AIMC Topic: Plastic Surgery Procedures

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Factors associated with complication of cranioplasty: CT-based risk assessment for early failure of autologous-bone cranioplasty.

Neurosurgical review
To determine whether preoperative noncontrast CT features predict early revision after autologous bone cranioplasty and to develop a simple CT-based risk framework. We retrospectively studied adults undergoing autologous cranioplasty at a single cent...

A nomogram for predicting renal function recovery after robotic-assisted ureteral reconstruction: development and comparative validation using traditional and machine learning models.

Journal of robotic surgery
OBJECTIVE: To develop, validate, and compare a Traditional Multivariable Logistic Regression model with a Machine Learning-based LASSO Regression Model for predicting significant renal function recovery in adult patients undergoing surgical repair fo...

Evaluating the performance of five large language models in answering Delphi consensus questions relating to patellar instability and medial patellofemoral ligament reconstruction.

BMC musculoskeletal disorders
PURPOSE: Artificial intelligence (AI) has become incredibly popular over the past several years, with large language models (LLMs) offering the possibility of revolutionizing the way healthcare information is shared with patients. However, to prevent...

Machine learning approaches overcome imbalanced clinical data for intraoral free flap monitoring.

Scientific reports
Free flap reconstruction is essential for treating intraoral defects; however, failure can lead to complex and prolonged complications. While various monitoring methods have been employed to prevent such situations, they are qualitative and sometimes...

Explainable artificial intelligence: enhancing decision-making in plastic surgery.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Artificial intelligence (AI) models increasingly influence plastic surgery practice through risk prediction, outcome forecasting, and treatment planning. However, their "black box" nature often prevents surgeons from understanding the reasoning behin...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Scientific reports
Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow quantification of flap volume and correlations with functional outcomes after surgery or post-opera...

Deep learning for orbital fracture detection and reconstruction: A systematic review on diagnostic accuracy and surgical planning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
OBJECTIVE: To systematically review the efficacy of deep learning (DL) models in detecting and reconstructing orbital fractures based on computed tomography (CT) imaging, assessing their diagnostic accuracy, processing time, and role in surgical plan...

Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

JCO clinical cancer informatics
PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Reconstruction Nomograms (SARCON),...

SCAI-Net: An AI-driven framework for optimized, fast, and resource-efficient skull implant generation for cranioplasty using CT images.

Computers in biology and medicine
Skull damage caused by craniectomy or trauma necessitates accurate and precise Patient-Specific Implant (PSI) design to restore the cranial cavity. Conventional Computer-Aided Design (CAD)-based methods for PSI design are highly infrastructure-intens...

Evaluating Large Language Model's accuracy in current procedural terminology coding given operative note templates across various plastic surgery sub-specialties.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Manual CPT coding from operative notes is a time-intensive process that adds to the administrative burden in healthcare. Large Language Models (LLMs) offer a promising solution, but their accuracy in assigning CPT codes based on full oper...