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SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability.

Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions are of great clinical significance, but the disproportionate dermatologist-patient ratio poses a significant problem in most developing nations. Therefore a novel deep architecture, named as SkiNe...

Feasibility of transthoracic esophagectomy with a next-generation surgical robot.

Robot-assisted minimal access surgery (MAS), compared with conventional MAS, has shown a number of benefits across several therapeutic indications but its use for transthoracic esophagectomy (TTE) requires further evaluation. Here, we report the first-in-human series of major esophageal resections performed using a next-generation tele-operated robotic surgical system in a single center. Robot-ass...

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets.

This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles usin...

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental s...

Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies.

UNLABELLED: was to develop a methodology for conducting post-registration clinical monitoring of software as a medical device based on artificial int...

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush inj...

Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review.

This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating th...

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking a...

Adverse events in the digital age and where to find them.

Exponential growth of health-related data collected by digital tools is a reality within pharmaceutical and medical device research and development. D...

Application of an EMG-Rehabilitation Robot in Patients with Post-Coronavirus Fatigue Syndrome (COVID-19)-A Feasibility Study.

This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation robot in patients with Post-Viral Fatigue (PVF) syndrome a...

Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images.

The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The ...

A real-time object detection model for orchard pests based on improved YOLOv4 algorithm.

Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard p...

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection.

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of m...

An interpretable neural network for outcome prediction in traumatic brain injury.

BACKGROUND: Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challe...

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.

We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pair...

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted f...

Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.

Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR)...

A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.

We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to expl...

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bon...

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based B...