Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference Article Swipe
Benjamin Fritz
,
Giuseppe Marbach
,
Francesco Civardi
,
Sandro F. Fucentese
,
Christian W. A. Pfirrmann
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/s00256-020-03410-2
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/s00256-020-03410-2
Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears. Materials and methods One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics. Results Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN ( p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741). Conclusion DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00256-020-03410-2
- https://link.springer.com/content/pdf/10.1007/s00256-020-03410-2.pdf
- OA Status
- hybrid
- Cited By
- 74
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3011530750
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3011530750Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00256-020-03410-2Digital Object Identifier
- Title
-
Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of referenceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-13Full publication date if available
- Authors
-
Benjamin Fritz, Giuseppe Marbach, Francesco Civardi, Sandro F. Fucentese, Christian W. A. PfirrmannList of authors in order
- Landing page
-
https://doi.org/10.1007/s00256-020-03410-2Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00256-020-03410-2.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s00256-020-03410-2.pdfDirect OA link when available
- Concepts
-
Medicine, Medial meniscus, Meniscus, Arthroscopy, Tears, Lateral meniscus, Orthopedic surgery, Kappa, Receiver operating characteristic, Radiology, Nuclear medicine, Surgery, Osteoarthritis, Pathology, Internal medicine, Incidence (geometry), Optics, Physics, Linguistics, Philosophy, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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74Total citation count in OpenAlex
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2025: 14, 2024: 16, 2023: 13, 2022: 18, 2021: 9Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.retrospectively | 28 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.98635706 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |