Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis
A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
|---|---|
| Enthalten in: |
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation - 48(2024), 2 vom: 30. März, Seite 126-131 |
| Sprache: |
Chinesisch |
|---|
| Beteiligte Personen: |
Xie, Kunjie [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Adolescent idiopathic scoliosis |
|---|
| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.12455/j.issn.1671-7104.230700 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370949277 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370949277 | ||
| 003 | DE-627 | ||
| 005 | 20240415233825.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240412s2024 xx |||||o 00| ||chi c | ||
| 024 | 7 | |a 10.12455/j.issn.1671-7104.230700 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1376.xml |
| 035 | |a (DE-627)NLM370949277 | ||
| 035 | |a (NLM)38605609 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a chi | ||
| 100 | 1 | |a Xie, Kunjie |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 15.04.2024 | ||
| 500 | |a Date Revised 15.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cobb angel | |
| 650 | 4 | |a X-ray images | |
| 650 | 4 | |a adolescent idiopathic scoliosis | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a diagnosis | |
| 700 | 1 | |a Lei, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Suping |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yaopeng |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Jincong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Yabo |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation |d 1997 |g 48(2024), 2 vom: 30. März, Seite 126-131 |w (DE-627)NLM093697007 |x 1671-7104 |7 nnns |
| 773 | 1 | 8 | |g volume:48 |g year:2024 |g number:2 |g day:30 |g month:03 |g pages:126-131 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.12455/j.issn.1671-7104.230700 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 48 |j 2024 |e 2 |b 30 |c 03 |h 126-131 | ||