AIMC Topic: Humans

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A Comparison of Endoscope-Assisted and Open Frontoorbital Distraction for the Treatment of Unicoronal Craniosynostosis.

Plastic and reconstructive surgery
BACKGROUND: Frontoorbital distraction osteogenesis (FODO) is an established surgical technique for patients with unicoronal craniosynostosis. The authors' institution has used an endoscope-assisted technique (endo-FODO) in recent years to decrease cu...

Patterns of Marijuana Use and Nicotine Exposure in Patients Seeking Elective Aesthetic Procedures.

Plastic and reconstructive surgery
BACKGROUND: With the increasing legalization and popularity of marijuana, it is frequently and sometimes unintentionally combined with nicotine-containing products. As a consequence, patients may fail to accurately report usage during preoperative ex...

Using Machine Learning to Select Breast Implant Volume.

Plastic and reconstructive surgery
BACKGROUND: In breast augmentation surgery, selection of the appropriate breast implant size is a crucial step that can greatly affect patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judg...

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes.

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

European radiology
OBJECTIVES: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework.

The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract.

Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.

CPT: pharmacometrics & systems pharmacology
Recently, the use of machine-learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures...

Mangled extremity: limb salvage for reconstruction versus primary amputation.

Current opinion in critical care
PURPOSE OF REVIEW: While MESS has historically influenced limb salvage versus amputation decisions, its universal applicability remains uncertain. With trauma systems expanding and advancements in trauma care, the need for a nuanced understanding of ...