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Related Concept Videos

Sutures of the Skull01:22

Sutures of the Skull

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The human skull is composed of several bones that come together to protect the brain and support the structures of the face. The junctions where these bones meet are called sutures.
Sutures are immobile joints between adjacent bones of the skull. The narrow gap between the bones is filled with dense, fibrous connective tissue that unites the bones. The long sutures located between the skull bones are not straight but instead follow irregular, tightly twisting paths. These twisting lines tightly...
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A Training and Testing System for Performing Vascular Reconstruction In Vitro
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Deep learning based suture training system.

Mohammed Mansour1, Eda Nur Cumak1, Mustafa Kutlu1

  • 1Department of Mechatronics Engineering, Sakarya University of Applied Sciences, Sakarya, Turkey.

Surgery Open Science
|August 21, 2023
PubMed
Summary
This summary is machine-generated.

Deep Learning (DL) models can accurately classify surgical suture success. The Xception model achieved 95% accuracy, offering a digital tool to improve trainee assessment and reduce errors.

Keywords:
ClassificationDeep learningSuture training

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Surgery
  • Surgical Skill Assessment

Background:

  • Surgical suturing is a core skill for medical and dental students.
  • Current suture skill assessment is subjective and lacks specific technique feedback.
  • Technological advancements enable objective measurement of surgical skills.

Purpose of the Study:

  • To evaluate the efficacy of Deep Learning (DL) techniques in assessing surgical suture success.
  • To develop an AI-driven tool for objective suture skill classification.

Main Methods:

  • Six Convolutional Neural Network (CNN) models (VGG16, VGG19, Xception, Inception, MobileNet, DensNet) were trained on a dataset of suture images.
  • Models were evaluated using precision, recall, and F1 scores for classifying successful vs. unsuccessful sutures.

Main Results:

  • The Xception model demonstrated the highest accuracy at 95%.
  • Other models showed varying accuracies: MobileNet (91%), DensNet (90%), Inception (84%), VGG16 (73%), and VGG19 (61%).
  • A graphical user interface was developed for real-time suture image evaluation.

Conclusions:

  • Deep Learning models can objectively and accurately assess surgical suture skills.
  • AI-powered assessment can minimize errors from inexperience and enhance physician efficiency.
  • Digitizing suture skill evaluation offers a scalable solution for medical training.