Transplantation

A discontinuity-preserving regularization for deep learning-based cardiac image registration.

. Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous studies, resulting in poor image registration performance. A new approach is proposed to handle discontinuity at the boundary and improve registration accuracy.. The proposed discontinuity-preserving regularization (DPR) term can maintain local disc...

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

IMPORTANCE: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology i...

Head and neck reconstruction in the vessel depleted neck using robot-assisted harvesting of the internal mammary vessels.

We report a novel technique of robot-assisted harvesting of the internal mammary vessels to provide effective recipient vessels in a patient with bila...

Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections.

Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have differe...

Complex computation from developmental priors.

Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nasce...

Robotic percutaneous coronary intervention (R-PCI): Time to focus on the pros and cons.

AIM: To assess the safety, efficiency, and device compatibility of the Second Generation Robotic System.

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects ...

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed s...

Glomerulus Detection Using Segmentation Neural Networks.

Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in k...

FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network.

An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which t...

New Generation of 3D Virtual Models with Perfusional Zones: Perioperative Assistance for the Best Pedicle Management during Robotic Partial Nephrectomy.

Selective clamping during robot-assisted partial nephrectomy (RAPN) may reduce ischemia-related functional impairment. The intraoperative use of 3D-vi...

Novel Port Placement in Robot-Assisted DIEP Flap Harvest Improves Visibility and Bilateral DIEP Access: Early Controlled Cohort Study.

SUMMARY: To minimize donor-site damage, robot-assisted (RA) deep inferior epigastric perforator (DIEP) flap harvest has been suggested. Current roboti...

The use of artificial intelligence to detect students' sentiments and emotions in gross anatomy reflections.

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to e...

Deep Learning-Enabled Morphometric Analysis for Toxicity Screening Using Zebrafish Larvae.

Toxicology studies heavily rely on morphometric analysis to detect abnormalities and diagnose disease processes. The emergence of ever-increasing vari...

Emerging MXene-Based Flexible Tactile Sensors for Health Monitoring and Haptic Perception.

Due to their potential applications in physiological monitoring, diagnosis, human prosthetics, haptic perception, and human-machine interaction, flexi...

Rapid estimation of patient-specific organ doses using a deep learning network.

BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks,...

Total robot-assisted recipient's surgery in living donor liver transplantation: First step towards the future.

Lee and colleagues describe a groundbreaking total robot-assisted explant hepatectomy followed by robotic engraftment for a patient requiring a living...

Probiotic Escherichia coli Nissle 1917 propelled micro-robot with pH sensitivity for hypoxia targeted intestinal tumor therapy.

Poor drug penetration in hypoxia area of solid tumor is a big challenge for intestinal tumor therapy and thus it is crucial to develop an effective st...

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery.

Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual...