Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Esophageal Perforation-II: Clinical Manifestations and Management

Esophageal Perforation-II: Clinical Manifestations and Management

Esophageal perforations manifest in various clinical forms, influenced by factors such as the perforation's cause and location (cervical, intrathoracic, or intra-abdominal), the extent of contamination, and potential injury to adjacent mediastinal structures. The timing between the perforation occurrence and treatment initiation also affects the clinical presentation.
Clinical Manifestations:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hematocrit Is Associated with Cancer-Related Fatigue in Colorectal Cancer Survivors: A Longitudinal Analysis.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2024

Same author

Early Warning Scores to Support Continuous Wireless Vital Sign Monitoring for Complication Prediction in Patients on Surgical Wards: Retrospective Observational Study.

JMIR perioperative medicine·2023

Same author

Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects.

Scientific data·2023

Same author

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models.

Diagnostics (Basel, Switzerland)·2023

Same author

Expectations of Continuous Vital Signs Monitoring for Recognizing Complications After Esophagectomy: Interview Study Among Nurses and Surgeons.

JMIR perioperative medicine·2021

Same author

Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2021

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Complication Prediction after Esophagectomy with Machine Learning.

Jorn-Jan van de Beld^1,2, David Crull², Julia Mikhal^2,3

¹Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands.

Diagnostics (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

Machine learning accurately predicts anastomotic leakage and pneumonia after esophagectomy (esophageal cancer surgery). These AI models offer early warnings, improving patient outcomes and surgical care.

Keywords:

clinical decision support esophagectomy multimodal machine learning temporal learning

More Related Videos

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

An Ivor Lewis Esophagectomy Designed to Minimize Anastomotic Complications and Optimize Conduit Function

An Ivor Lewis Esophagectomy Designed to Minimize Anastomotic Complications and Optimize Conduit Function

Published on: April 17, 2020

Related Experiment Videos

Last Updated: Jul 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

An Ivor Lewis Esophagectomy Designed to Minimize Anastomotic Complications and Optimize Conduit Function

An Ivor Lewis Esophagectomy Designed to Minimize Anastomotic Complications and Optimize Conduit Function

Published on: April 17, 2020

Area of Science:

Medical technology
Artificial intelligence in medicine
Surgical oncology

Background:

Esophagectomy is a key treatment for esophageal cancer, but carries a high risk of postoperative complications.
Anastomotic leakage and pneumonia are significant concerns following esophagectomy, impacting patient recovery and outcomes.

Purpose of the Study:

To evaluate the efficacy of machine learning (ML) models in predicting anastomotic leakage and pneumonia.
To assess the predictive performance of ML for these complications up to two days in advance.

Main Methods:

Utilized a dataset of 417 patients who underwent esophagectomy between 2011 and 2021.
Incorporated multimodal temporal data including laboratory results, vital signs, thorax images, and preoperative patient characteristics.
Developed and validated ML models to predict anastomotic leakage and pneumonia.

Main Results:

The best ML models achieved AUROCs of 0.87 and 0.82 for predicting anastomotic leakage 1 and 2 days ahead, respectively.
For pneumonia prediction, the models achieved AUROCs of 0.74 and 0.61 for 1 and 2 days ahead, respectively.
Demonstrated strong predictive capabilities for both complications.

Conclusions:

Machine learning models can effectively predict anastomotic leakage and pneumonia following esophagectomy.
Early prediction of these complications using ML can potentially improve patient management and outcomes.
Highlights the potential of AI in enhancing postoperative care in esophageal cancer surgery.