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Automatic Assessment of AI-produced 3D Medical Image Segmentations of the Scapula using Deep Learning

5 pagesPublished: December 17, 2024

Abstract

Artificial intelligence (AI) and machine learning (ML) take an ever-growing place in medical care. Anatomical segmentation and reconstruction is one of the fields where ML reveals to be very efficient. Yet, verification of ML results still requires human verification and correction especially on pathologic morphologies. We propose an automatic assessment of AI-generated scapular reconstructions. Based on deep learning (DL), it separates predictions requiring little to no revision from predictions where corrected voxels represent more than 1% of the scapula, with an accuracy of 80%.

Keyphrases: artificial intelligence, automated evaluation, deep learning, image reconstruction, total shoulder arthroplasty

In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 211-215.

BibTeX entry
@inproceedings{CAOS2024:Automatic_Assessment_AI_produced,
  author    = {Garance Thoviste and Lhoussein Axel Mabrouk and Fabrice Bertrand and Clément Daviller},
  title     = {Automatic Assessment of AI-produced 3D Medical Image Segmentations of the Scapula using Deep Learning},
  booktitle = {Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/v2l9},
  doi       = {10.29007/9d1b},
  pages     = {211-215},
  year      = {2024}}
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