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Related Experiment Video

Updated: Apr 30, 2026

A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy
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A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy

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Predicting Pediatric Urological Surgery Duration Through Multimodal Patient-Physician Feature Fusion: Deep Learning

Yonggen Zhao1,2, Ruoge Lin3, Yiying Sun1,2

  • 1National Clinical Research Center for Children and Adolescents' Health and Diseases, Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Rd, Hangzhou, 310052, China, 86 13588773370.

JMIR Medical Informatics
|April 28, 2026
PubMed
Summary

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Dual-stage deep learning framework for neuroblastoma differentiation by integrating cell segmentation and multiscale modeling.

World journal of pediatric surgery·2026
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Integrative Genomic Profiling of Pediatric Solid Tumors Reveals Clinically Relevant Variants and Chromosomal Arm Aneuploidies Signatures.

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A causal forest model integrating quantitative CT scores to predict benefit from flexible bronchoscopy in pediatric Mycoplasma pneumoniae pneumonia: a two-center retrospective study.

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Scientific data·2025
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Automated Grading of Vesicoureteral Reflux (VUR) Using a Dual-Stream CNN Model with Deep Supervision.

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This summary is machine-generated.

This study developed a new AI framework to accurately predict pediatric urology surgery times. The model significantly improves operating room scheduling and resource allocation by considering unique pediatric factors.

Area of Science:

  • Artificial Intelligence in Medicine
  • Pediatric Urology
  • Surgical Workflow Optimization

Background:

  • Accurate surgical duration prediction is crucial for operating room scheduling and resource management.
  • Existing prediction models are often inadequate for pediatric urology due to children's unique anatomical and developmental traits.

Purpose of the Study:

  • To develop and validate a specialized prediction framework for estimating pediatric urological surgery durations.
  • To enhance the precision of operating room scheduling and surgical resource allocation in pediatric urology.

Main Methods:

  • Integrated multisource heterogeneous data, including patient demographics, surgical details, surgeon information, and clinical notes.
  • Utilized large language models for extracting semantic information from unstructured text and a multihead perceptron for feature fusion.
Keywords:
deep neural networkmedical informaticsmultihead perceptronoperating room managementsurgical duration prediction

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  • Explicitly modeled pediatric-specific variables like developmental stage and urinary tract malformation severity.

    • Main Results:

    • Achieved a mean absolute error of 11.39 minutes and a root mean square error of 15.58 minutes, outperforming existing methods.
    • Demonstrated superior feature representation using Qwen-based structured preprocessing with text embeddings.
    • Identified primary surgical procedure, surgical plan, and preoperative diagnosis as key predictive factors.

    Conclusions:

    • The developed framework significantly enhances surgical duration prediction accuracy in pediatric urology through innovative feature engineering and a tailored model architecture.
    • Provides robust technical support for precision operating room scheduling.
    • Offers significant clinical value by improving the efficiency of surgical resource utilization.