Hospital-Based Medicine

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation.

BACKGROUND: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific tasks like calling for emergencies, monitoring patient health, etc. while the patients' limitations are ignored. Though some patient interaction...

Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning.

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass op...

Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale.

BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative predi...

Recent Advances in Touch Sensors for Flexible Wearable Devices.

Many modern user interfaces are based on touch, and such sensors are widely used in displays, Internet of Things (IoT) projects, and robotics. From la...

Liver Damage Is Related to the Degree of Being Underweight in Anorexia Nervosa and Improves Rapidly with Weight Gain.

Background: The present study investigates the relationship between hypertransaminasemia and malnutrition on the basis of a very large number of patie...

The Role of AirSeal in Robotic Urologic Surgery: A Systematic Review.

The adoption of a valveless trocar system in robotic surgery has allowed for stable pneumoperitoneum and constant smoke evacuation. The reported bene...

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifyi...

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and arti...

A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data.

Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses ...

Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning.

This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis ...

Results of robotic TAPP and conventional laparoscopic TAPP in an outpatient setting: a cohort study in Switzerland.

PURPOSE: Recently, robotic surgery has been increasingly performed in hernia surgery. Although feasibility and safety of robot-assisted inguinal herni...

Developing image analysis methods for digital pathology.

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite n...

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specif...

Retzius-Sparing Robot-Assisted Radical Prostatectomy.

The technique of Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) and initial experience with it at a single center are provided. The te...

Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol.

INTRODUCTION: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug ...

Sparking the Interest of Girls in Computer Science via Chemical Experimentation and Robotics: The Qui-Bot HO Case Study.

We report a new learning approach in science and technology through the Qui-Bot HO project: a multidisciplinary and interdisciplinary project develope...

Robot-assisted Radical Prostatectomy with the Novel Hugo Robotic System: Initial Experience and Optimal Surgical Set-up at a Tertiary Referral Robotic Center.

We describe the first five robot-assisted radical prostatectomies (RARPs) performed with the new Hugo RAS system (Medtronic, Minneapolis, MN, USA) in ...

Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.

Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complic...

Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A Multidisciplinary Consensus.

BACKGROUND: Artificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to ...