Surgery

Weakly supervised treatment selection: Machine learning models for appropriate surgical planning of submandibular stones.

There is a gap in real-world clinical adoption of machine learning (ML) solutions due to the inherent uncertainty and variability in treatment outcomes. To bridge this gap, we present a novel approach to the problem of medical treatment selection using ML models and we apply it to the case of submandibular sialolithiasis treatment. The study introduces a weakly supervised learning framework which ...

Clinical applications of artificial intelligence in hand surgery: A systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, yet its clinical integration in hand surgery remains variable and incompletely validated. This systematic review and meta-analysis evaluated current AI applications in hand surgery and benchmarked performance against human comparators where available. METHODS: Following PRISMA 2020 guidelines, PubMed/MEDLINE, Embase, Web...

Artificial intelligence applications for enhancing patient self-care education following sternotomy: Development and initial evaluation.

BACKGROUND: Research indicates that over 12% of patients undergoing coronary artery bypass grafting and more than 14% of patients undergoing surgical ...

Predicting Postoperative Discharge Status and Readmissions in Spinal Metastatic Disease Using Machine Learning Models.

Operative management of spinal metastatic disease is largely for symptom palliation rather than curative and revolves around the expectation that post...

Dysfunctional resting state network connectivity predicts postoperative delirium after major surgery.

BACKGROUND: Postoperative delirium is associated with increased morbidity, mortality, future cognitive decline, or dementia. Understanding the neural ...

Artificial Intelligence and Skills Training in Resident Education.

Artificial intelligence (AI) is rapidly transforming surgical education. AI is defined by the characteristics of objectivity, live feedback, and adapt...

The Role of Perioperative Artificial Intelligence in Colon and Rectal Surgery.

Artificial intelligence (AI) is rapidly transforming surgical practice with growing applications in colon and rectal surgery. This review explores per...

DVAP-Reg: Dual-view anatomical prior-driven cross-dimensional registration for spinal surgery navigation.

2D-3D cross-dimensional registration serves as a critical technology in spinal surgery navigation, with profound implications for enhancing surgical p...

Revolutionizing early detection of TNBC: The rise of non-invasive biomarkers and next-gen diagnostic strategies.

Triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer (BC), which accounts for 10-20 % of BC incidences, and its early d...

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with succes...

Utility of Artificial Intelligence-Driven Chatbots in Suggesting Solutions to Stapling Misadventures in Colorectal Surgery.

BackgroundColorectal surgery stapling misadventures are fairly common, potentially leading to serious complications. Although artificial intelligence ...

A Fully Automated Multistage Deep Learning System for Lenke Classification: Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis.

BACKGROUND: The Lenke classification for adolescent idiopathic scoliosis (AIS) has interobserver variability due to subjective clinical assessment. We...

Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases.

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Prognosis is significantly worsened in patients with colorectal liv...

Improving Acute Care Surgery with Artificial Intelligence: A Practical Review.

Artificial intelligence (AI) and machine learning are poised to transform trauma care across the entire continuum, from prehospital triage to postoper...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Pirinixic Acid Protects Against Hemorrhagic Shock by Regulating Neutrophil-Associated Key Genes.

BACKGROUND: The uncontrolled inflammatory cascade triggered by hemorrhagic shock (HS) can exacerbate tissue damage and organ dysfunction. Neutrophils,...

Computed Tomography-Pathology Deep Learning Model for the Precise Prediction of Recurrence in Pathological Stage IA Lung Adenocarcinoma.

BACKGROUND: The postoperative prognosis of pathological stage IA lung adenocarcinoma (LUAD) exhibits significant heterogeneity. While the tumor node m...

Revolutionizing salivary biomarkers through machine learning and artificial intelligence.

Chronic diseases remain major global health burdens, and early detection is essential for preventing progression and reducing complications. Saliva, a...

Urinary Tumor DNA to Identify Candidates for Repeat Transurethral Resection in Non-Muscle-Invasive Bladder Cancer.

Repeat transurethral resection of bladder tumor (re-TURBT) is commonly recommended for patients with non-muscle-invasive bladder cancer (NMIBC) with h...