ArticleNPJ digital medicine2025
Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Trial
- Technical Feasibility of a Multimodal Electro-Optical and Infrared Imaging Pipeline for Postoperative Wound Assessment After Total Knee Arthroplasty: Formative Evaluation Study.JMIR formative research · 2026Article
- Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.Surgical endoscopy · 2026Review
- Smartphone-Based Postoperative Wound Assessment Following Laparoscopic Surgery in a Resource-Limited Setting: A Prospective Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.Journal of robotic surgery · 2026Article
- From prediction to partnership: positioning machine learning as a decision-support tool for nursing risk assessment in postoperative infection prevention.International journal of surgery (London, England) · 2026Article
- SurgWound-Bench: a benchmark for surgical wound diagnosis.NPJ digital medicine · 2026Article
- Intelligent therapeutic robotic-assisted surgery as the next frontier of precision oncology.Frontiers in oncology · 2026Review
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- DiabCompSepsAI: Integrated AI Model for Early Detection and Prediction of Postoperative Complications in Diabetic Patients-Using a Random Forest Classifier.Journal of clinical medicine · 2025Article
- Med-Diffusion: Diffusion Model-Based Imputation of Multimodal Sensor Data for Surgical Patients.Sensors (Basel, Switzerland) · 2025Article
- Imaging-based Surgical Site Infection Detection Using Artificial Intelligence.Annals of surgery · 2025Article
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Remote monitoring is essential for healthcare digital transformation, however, this poses greater burdens on healthcare providers to review and respond as the data collected expands. This study developed a multimodal neural network to automate assessments of patient-generated data from remote postoperative wound monitoring. Two interventional studies including adult gastrointestinal surgery patients collected wound images and patient-reported outcome measures (PROMs) for 30-days postoperatively. Neural networks for PROMs and images were combined to predict surgical site infection (SSI) diagnosis within 48 h. The multimodal neural network model to predict confirmed SSI within 48 h remained comparable to clinician triage (0.762 [0.690-0.835] vs 0.777 [0.721-0.832]), with an excellent performance on external validation. Simulated usage indicated an 80% reduction in staff time (51.5 to 9.1 h) without compromising diagnostic accuracy. This multimodal approach can effectively support remote monitoring, alleviating provider burden while ensuring high-quality postoperative care.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.