Surgery

Variability of Guidelines and Disclosures for AI-Generated Content in Top Surgical Journals.

: When properly utilized, artificial intelligence generated content (AIGC) may improve virtually every aspect of research, from data gathering to synthesis. Nevertheless, when used inappropriately, the use of AIGC may lead to the dissemination of inaccurate information and introduce potential ethical concerns.: Cross-sectional. Study Sample: 65 top surgical journals. Data Collection: Each journals...

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-sma...

Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.

BACKGROUND: Ahmed valve implantation demonstrated an increasing proportion in glaucoma surgery, but predicting the successful maintenance of target in...

Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning.

BACKGROUND: The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during ...

Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitoring of changes in the lateral brain ventricles of ...

Automated detection of steps in videos of strabismus surgery using deep learning.

BACKGROUND: Learning to perform strabismus surgery is an essential aspect of ophthalmologists' surgical training. Automated classification strategy fo...

The relationship between design-based lateralization, humeral bearing design, polyethylene angle, and patient-related factors on surgical complications after reverse shoulder arthroplasty: a machine learning analysis.

BACKGROUND: Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance o...

Surgical Intelligence Can Lead to Higher Adoption of Best Practices in Minimally Invasive Surgery.

OBJECTIVE: To examine the use of surgical intelligence for automatically monitoring critical view of safety (CVS) in laparoscopic cholecystectomy (LC)...

Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is crit...

Artificial intelligence applied to laparoscopic cholecystectomy: what is the next step? A narrative review.

Artificial Intelligence (AI) is playing an increasing role in several fields of medicine. AI is also used during laparoscopic cholecystectomy (LC) sur...

Skin-inspired, sensory robots for electronic implants.

Drawing inspiration from cohesive integration of skeletal muscles and sensory skins in vertebrate animals, we present a design strategy of soft robots...

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the...

Development of machine learning models to predict perioperative blood transfusion in hip surgery.

BACKGROUND: Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk ...

An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care.

With the introduction of an artificial intelligence-based dashboard into the clinic, the project SURGE-Ahead responds to the importance of improving p...

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SA...

Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

BACKGROUND: Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML alg...

Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis.

The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. ...

Real-time haptic characterisation of Hunt-Crossley model based on radial basis function neural network for contact environment.

Dynamic soft tissue characterisation is an important element in robotic minimally invasive surgery. This paper presents a novel method by combining ne...

Empowering gynaecologists with Artificial Intelligence: Tailoring surgical solutions for fibroids.

BACKGROUND: In recent years, the integration ofArtificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising...

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery.

Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in rob...