AIMC Topic: Models, Anatomic

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The CT-based deep learning model outperforms traditional anatomical classification models in preoperatively predicting complications and risk grade in partial nephrectomy.

World journal of urology
PURPOSE: A deep learning model integrating CT radiomics and clinical features was developed to predict perioperative complications and risk grade in patients undergoing partial nephrectomy, and was compared to traditional anatomical classification mo...

Automatic segmentation of male pelvic floor soft tissue structures for anatomical simulation and morphological assessment in lower rectal cancer surgery.

Techniques in coloproctology
BACKGROUND: Pelvic anatomy is a complex network of organs that varies between individuals. Understanding the anatomy of individual patients is crucial for precise rectal cancer surgeries. Therefore, developing technology that can allow visualization ...

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

IEEE transactions on neural networks and learning systems
Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each indivi...

3D face reconstruction for maxillofacial surgery based on morphable models and neural networks: A preliminary assessment for anthropometry accuracy.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
OBJECTIVES: This study aimed to evaluate the anthropometric accuracy of 3D face reconstruction based on neural networks (3DFRBN) using 2D images, including the assessment of global errors and landmarks, as well as linear and angular measurements.

Smartphone-based scans of palate models of newborns with cleft lip and palate: Outlooks for three-dimensional image capturing and machine learning plate tool.

Orthodontics & craniofacial research
OBJECTIVES: To evaluate the performance of smartphone scanning applications (apps) in acquiring 3D meshes of cleft palate models. Secondarily, to validate a machine learning (ML) tool for computing automated presurgical plate (PSP).

Integrating Artificial Intelligence Into the Visualization and Modeling of Three-Dimensional Anatomy in Pediatric Surgical Patients.

Journal of pediatric surgery
BACKGROUND: Pediatric surgeons often treat patients with complex anatomical considerations due to congenital anomalies or distortion of normal structures by solid organ tumors. There are multiple applications for three-dimensional visualization of th...