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European Spine Journal

Erschienen in:

12.03.2022 | Supplement article

Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation

verfasst von: Tomaž Vrtovec, Bulat Ibragimov

Erschienen in: European Spine Journal | Ausgabe 8/2022

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Abstract

Purpose

To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL).

Methods

Three databases (PubMed, WoS and Scopus) were queried for records using keywords related to DL and measurement of sagittal balance. After screening the resulting 529 records that were augmented with specific web search, 34 studies published between 2017 and 2022 were included in the final review, and evaluated from the perspective of the observed sagittal spinopelvic parameters, properties of spine image datasets, applied DL methodology and resulting measurement performance.

Results

Studies reported DL measurement of up to 18 different spinopelvic parameters, but the actual number depended on the image field of view. Image datasets were composed of lateral lumbar spine and whole spine X-rays, biplanar whole spine X-rays and lumbar spine magnetic resonance cross sections, and were increasing in size or enriched by augmentation techniques. Spinopelvic parameter measurement was approached either by landmark detection or structure segmentation, and U-Net was the most frequently applied DL architecture. The latest DL methods achieved excellent performance in terms of mean absolute error against reference manual measurements (~ 2° or ~ 1 mm).

Conclusion

Although the application of relatively complex DL architectures resulted in an improved measurement accuracy of sagittal spinopelvic parameters, future methods should focus on multi-institution and multi-observer analyses as well as uncertainty estimation and error handling implementations for integration into the clinical workflow. Further advances will enhance the predictive analytics of DL methods for spinopelvic parameter measurement.

Level of Evidence I

Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.

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https://www.webofknowledge.com/; query: (ALL = (artificial intelligence) OR ALL = (deep learning)) AND (ALL = (sagittal balance) OR ALL = (spinopelvic balance) OR ALL = (pelvic incidence) OR ALL = (lumbar lordosis) OR ALL = (thoracic kyphosis) OR ALL = (cervical lordosis) OR ALL = (Cobb angle)) AND DOP = (2012/2022).

https://www.scopus.com/; query: (ALL("artificial intelligence") OR ALL("deep learning")) AND (ALL("sagittal balance") OR ALL("spinopelvic balance") OR ALL("pelvic incidence") OR ALL("lumbar lordosis") OR ALL("thoracic kyphosis") OR ALL("cervical lordosis") OR ALL("Cobb angle")) AND PUBYEAR AFT 2011.

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Titel: Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation
verfasst von: Tomaž Vrtovec
Bulat Ibragimov
Publikationsdatum: 12.03.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Spine Journal / Ausgabe 8/2022
Print ISSN: 0940-6719
Elektronische ISSN: 1432-0932
DOI: https://doi.org/10.1007/s00586-022-07155-5

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Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation

Abstract

Purpose

Methods

Results

Conclusion

Level of Evidence I

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Neu im Fachgebiet Orthopädie und Unfallchirurgie

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TEP mit Roboterhilfe führt nicht zu größerer Zufriedenheit

Update Orthopädie und Unfallchirurgie

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

Level of Evidence I

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