Surgery

Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.

PURPOSE: Meningiomas are the most common primary brain tumour and account for over one-third of cases. Traditionally, estimations of morbidity and mortality following surgical resection have depended on subjective assessments of various factors, including tumour volume, location, WHO grade, extent of resection (Simpson grade) and pre-existing co-morbidities, an approach fraught with subjective var...

Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study.

BACKGROUND: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics i...

Spontaneous Hepatic Rupture Complicating Preeclampsia and HELLP Syndrome: A Case Report.

Spontaneous hepatic rupture is a rare complication that occurs in pregnant mothers with HELLP syndrome, or preeclampsia with severe features, or ecla...

Efficacy evaluation of a minimally invasive surgical procedure (oblique lateral interbody fusion) for lumbar spinal tuberculosis-retrospective cohort study.

OBJECTIVE: In the current study, to demonstrate the advantages of oblique lateral interbody fusion (OLIF), we focused on the therapeutics for lumbar s...

Efficacy of preoperative prophylactic application of betamethasone on postoperative nausea and vomiting in patients undergoing total knee arthroplasty: a prospective randomized controlled trial.

BACKGROUND: The demand for total knee arthroplasty (TKA) is increasing, yet postoperative nausea and vomiting (PONV) significantly hinder patient reco...

Pixels to precision: Neuroradiology's leap into 3D printing for personalized medicine.

The realm of precision medicine, particularly its application within various sectors, shines notably in neuroradiology, where it leverages the advance...

Biochemical, Radiographic, or Pathologic Response to Neoadjuvant Chemotherapy in Resected Pancreatic Cancer: Which Is Best?

OBJECTIVE: To examine the optimal method of assessing response to neoadjuvant therapy (NAT) in patients with operable pancreatic ductal adenocarcinoma...

Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

PURPOSE: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. E...

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

BACKGROUND AND OBJECTIVE: Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians ...

Deep learning-assistance significantly increases the detection sensitivity of neurosurgery residents for intracranial aneurysms in subarachnoid hemorrhage.

OBJECTIVE: The purpose of this study was to evaluate the effectiveness of a deep learning model (DLM) in improving the sensitivity of neurosurgery res...

Multibody system dynamics for bio-robotic design and simulation based on inching-locomotion caterpillar's gait: MBD-ILAR method.

Inching-locomotion caterpillars (ILAR) inspire the design of 'inch-worm' robots with biomimicry features, that can be adapted to different environment...

A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.

OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate t...

Prediction of postoperative stroke in patients experienced coronary artery bypass grafting surgery: a machine learning approach.

BACKGROUND: Coronary artery bypass grafting (CABG) surgery has been a widely accepted method for treating coronary artery disease. However, its postop...

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brai...

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitati...

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. ...

Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

BACKGROUND: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the ...

Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes.

OBJECTIVE: Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determi...

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.

The presence, location, and extent of prostate cancer is assessed by pathologists using H&E-stained tissue slides. Machine learning approaches can acc...