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Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to

Robert R Ileșan1, Michel Beyer1,2, Christoph Kunz1

  • 1Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland.

Bioengineering (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

Developing in-house medical image segmentation software achieved high accuracy and significantly reduced segmentation time compared to commercial systems. Further research and collaboration are needed for full clinical acceptance of automated methods.

Keywords:
3D virtual reconstructionCBCTCTConvolutional Neural NetworksCranio-Maxillofacial surgeryDICOMartificial intelligencecomparisonin-housemandiblepatch sizesegmentationsoftware

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Area of Science:

  • Medical imaging
  • Artificial Intelligence
  • Computer-aided diagnosis

Background:

  • Manual and semi-automated medical image segmentation are time-consuming, subjective, and require expert knowledge.
  • Fully automated segmentation using Convolutional Neural Networks (CNNs) is gaining importance for efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate an in-house automated medical image segmentation software.
  • To compare the performance and efficiency of the in-house software against commercial systems, an inexperienced user, and an expert ground truth.

Main Methods:

  • Development of a novel in-house CNN-based segmentation model.
  • Comparative analysis of segmentation accuracy (Dice Similarity Coefficient) and time efficiency against established cloud-based commercial software.
  • Validation against expert and inexperienced user segmentations.

Main Results:

  • The in-house model achieved 94.24% accuracy, comparable to the best commercial software (0.912-0.949 Dice Similarity Coefficient).
  • The in-house software demonstrated a significantly shorter mean segmentation time of 2'03″ compared to commercial systems (3'54″ to 85'54″).
  • The development process highlighted challenges in creating clinically relevant automated solutions.

Conclusions:

  • Fully automated medical image segmentation software can achieve high accuracy and efficiency.
  • Collaboration between academia and industry is crucial for advancing automated segmentation towards clinical acceptance.
  • Further research is necessary to overcome existing challenges and ensure widespread adoption in clinical practice.