AIMC Topic: Humans

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Development and Validation of the Metric-Based Assessment of a Robotic Dissection Task on an Avian Model.

The Journal of surgical research
INTRODUCTION: The introduction of robot-assisted surgical devices requires the application of objective performance metrics to verify performance levels.

Propensity-Matched Analysis of the Short-Term Outcome of Robot-Assisted Minimally Invasive Esophagectomy Versus Conventional Thoracoscopic Esophagectomy in Thoracic Esophageal Cancer.

World journal of surgery
BACKGROUND: In this matched-cohort study, we investigated the short-term outcome of robot-assisted minimally invasive esophagectomy (RAMIE) compared with conventional minimally invasive thoracoscopic esophagectomy (MIE) in esophageal cancer patients.

Robot-assisted liver resection: the real benefit so far.

Langenbeck's archives of surgery
BACKGROUND: Minimally invasive liver resection is associated with lower perioperative morbidity and shorter hospital stay. However, the added benefit of the robotic platform over conventional laparoscopy is a matter of ongoing investigation.

Insomnia disorder diagnosed by resting-state fMRI-based SVM classifier.

Sleep medicine
BACKGROUND: The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to characterize the nocturnal sleep disturbance, identify daytime impairment, and determine the course...

Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors.

Computers in biology and medicine
Chromosome aberration (CA) is a serious genotoxicity of a compound, leading to carcinogenicity and developmental side effects. In the present manuscript, we developed a QSAR model for CA prediction using artificial intelligence methodologies. The rel...

Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists.

European journal of radiology
PURPOSE: To compare the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT).

Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.

Magnetic resonance imaging
BACKGROUND: T2-weighted imaging (T2WI) is a key sequence of MRI studies of the pancreas. The single-shot fast spin echo (single-shot FSE) sequence is an accelerated form of T2WI. We hypothesized that denoising approach with deep learning-based recons...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Mathematical biosciences
Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficient anticoagulant and the appropriate dosage regimen remain a complex problem because of the intersubj...

Deep learning-based plane pose regression in obstetric ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a major challenge in skill acquisition. We aim to build a US plane local...

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

Sensors (Basel, Switzerland)
The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this rese...