Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic
© 2024 American Institute of Ultrasound in Medicine..
OBJECTIVES: This study was to evaluate the application of automatic measurement based on convolutional neural network (CNN) technology in intracavitary ultrasound cine of anterior pelvic.
METHODS: A total of 500 patients who underwent pelvic floor ultrasound examination at Peking University Shenzhen Hospital from July 2021 to February 2022 were retrospectively retrieved by the picture archiving and communication system (PACS) system, and 300 cases were used as a training set. The training set was labeled by three experienced ultrasound physicians to train CNN models and develop an automatic measurement software. The remaining 200 cases were used as a test set. Automatic measurement software identified relevant anatomical structures frame by frame and determined the two frames with the greatest difference, calculated the bladder neck descent (BND), urethral rotation angle (URA), and retrovesical angle (RA). Meanwhile, two experienced ultrasound physicians evaluated the resting frame and the maximum Valsalva frame on the cines by manual visual evaluation, labeled the anatomical structures in the corresponding frame, such as the inferoposterior margin of pubic symphysis, the mid-axis of pubic symphysis, bladder contour, and urethra in the front, and calculated BND, URA, and RA. Considering that the residual urine volume (RUV) in the bladder may affect the results, enrolled patients were grouped according to the RUV (10-50 mL, 50-100 mL, and >100 mL). The consistency of the results by automatic measurement and manual visual evaluation was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman graph.
RESULTS: Of the 200 cases in the test set, 120 cases were successfully identified by the CNN automatic software with a 60% recognition rate. In the case of successful identification, the ICC of manual visual evaluation measurement and automatic measurement was 0.936 (BND), 0.911 (URA), 0.756 (RA in rest), and 0.877 (RA at maximum Valsalva), respectively. In addition, the RUV had a negligible effect on the consistency. The Bland-Altman plot shows the proportion of samples outside the limit was below 5%.
CONCLUSIONS: CNN-based automatic measurement software exhibited high reliability in anterior pelvic measurement, which results in a significantly enhanced measurement efficiency.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine - 43(2024), 4 vom: 28. März, Seite 671-681 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wei, Ni-Ya [VerfasserIn] |
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| Links: |
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| Themen: |
Anterior pelvic |
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| Anmerkungen: |
Date Completed 19.03.2024 Date Revised 19.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jum.16392 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM366765604 |
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| 100 | 1 | |a Wei, Ni-Ya |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic |
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| 500 | |a Date Revised 19.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024 American Institute of Ultrasound in Medicine. | ||
| 520 | |a OBJECTIVES: This study was to evaluate the application of automatic measurement based on convolutional neural network (CNN) technology in intracavitary ultrasound cine of anterior pelvic | ||
| 520 | |a METHODS: A total of 500 patients who underwent pelvic floor ultrasound examination at Peking University Shenzhen Hospital from July 2021 to February 2022 were retrospectively retrieved by the picture archiving and communication system (PACS) system, and 300 cases were used as a training set. The training set was labeled by three experienced ultrasound physicians to train CNN models and develop an automatic measurement software. The remaining 200 cases were used as a test set. Automatic measurement software identified relevant anatomical structures frame by frame and determined the two frames with the greatest difference, calculated the bladder neck descent (BND), urethral rotation angle (URA), and retrovesical angle (RA). Meanwhile, two experienced ultrasound physicians evaluated the resting frame and the maximum Valsalva frame on the cines by manual visual evaluation, labeled the anatomical structures in the corresponding frame, such as the inferoposterior margin of pubic symphysis, the mid-axis of pubic symphysis, bladder contour, and urethra in the front, and calculated BND, URA, and RA. Considering that the residual urine volume (RUV) in the bladder may affect the results, enrolled patients were grouped according to the RUV (10-50 mL, 50-100 mL, and >100 mL). The consistency of the results by automatic measurement and manual visual evaluation was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman graph | ||
| 520 | |a RESULTS: Of the 200 cases in the test set, 120 cases were successfully identified by the CNN automatic software with a 60% recognition rate. In the case of successful identification, the ICC of manual visual evaluation measurement and automatic measurement was 0.936 (BND), 0.911 (URA), 0.756 (RA in rest), and 0.877 (RA at maximum Valsalva), respectively. In addition, the RUV had a negligible effect on the consistency. The Bland-Altman plot shows the proportion of samples outside the limit was below 5% | ||
| 520 | |a CONCLUSIONS: CNN-based automatic measurement software exhibited high reliability in anterior pelvic measurement, which results in a significantly enhanced measurement efficiency | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a anterior pelvic | |
| 650 | 4 | |a bladder neck descent | |
| 650 | 4 | |a convolutional neural network | |
| 650 | 4 | |a retrovesical angle | |
| 650 | 4 | |a urethral rotation angle | |
| 700 | 1 | |a Li, Xiao-Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Xi-Duo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xin-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Rui-Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yue |e verfasserin |4 aut | |
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