Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps
ObjectivePhotoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preli...
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| Format: | Article |
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Editorial Office of Chinese Journal of Medical Instrumentation
2024-07-01
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| Series: | Zhongguo yiliao qixie zazhi |
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| Online Access: | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.230624 |
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| _version_ | 1846163565017300992 |
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| author | Jing YANG Xinlei YANG Yuwei GAO Chunlei ZHANG Di WANG Tao SONG |
| author_facet | Jing YANG Xinlei YANG Yuwei GAO Chunlei ZHANG Di WANG Tao SONG |
| author_sort | Jing YANG |
| collection | DOAJ |
| description | ObjectivePhotoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method’s ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries. MethodsData were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model. These datasets were then trained, validated, and tested using 1D-CNN. ResultsAs the degree of arterial embolization increased, the PPG amplitude upstream of the embolization site progressively increased, while the downstream amplitude progressively decreased, and the gap between the upstream and downstream amplitudes at the embolization site progressively widened. 1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model, achieving average accuracies of 98.36% and 95.90%, respectively. ConclusionThe combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery. |
| format | Article |
| id | doaj-art-912e96d7b235437c9f4863dcdd3f1b83 |
| institution | Kabale University |
| issn | 1671-7104 |
| language | zho |
| publishDate | 2024-07-01 |
| publisher | Editorial Office of Chinese Journal of Medical Instrumentation |
| record_format | Article |
| series | Zhongguo yiliao qixie zazhi |
| spelling | doaj-art-912e96d7b235437c9f4863dcdd3f1b832024-11-19T06:09:14ZzhoEditorial Office of Chinese Journal of Medical InstrumentationZhongguo yiliao qixie zazhi1671-71042024-07-0148441942510.12455/j.issn.1671-7104.2306242023-0624Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of FlapsJing YANG0Xinlei YANG1Yuwei GAO2Chunlei ZHANG3Di WANG4Tao SONG5School of Stomatology, Harbin Medical University, Harbin, 150000The First Affiliated Hospital of Harbin Medical University, Harbin, 150000Stomatological Hospital of Harbin Medical University, Harbin, 150000School of Stomatology, Harbin Medical University, Harbin, 150000School of Stomatology, Harbin Medical University, Harbin, 150000Stomatological Hospital of Harbin Medical University, Harbin, 150000ObjectivePhotoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method’s ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries. MethodsData were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model. These datasets were then trained, validated, and tested using 1D-CNN. ResultsAs the degree of arterial embolization increased, the PPG amplitude upstream of the embolization site progressively increased, while the downstream amplitude progressively decreased, and the gap between the upstream and downstream amplitudes at the embolization site progressively widened. 1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model, achieving average accuracies of 98.36% and 95.90%, respectively. ConclusionThe combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery.https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.230624photoplethysmographydeep learningflap |
| spellingShingle | Jing YANG Xinlei YANG Yuwei GAO Chunlei ZHANG Di WANG Tao SONG Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps Zhongguo yiliao qixie zazhi photoplethysmography deep learning flap |
| title | Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps |
| title_full | Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps |
| title_fullStr | Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps |
| title_full_unstemmed | Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps |
| title_short | Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps |
| title_sort | application of photoplethysmography combined with deep learning in postoperative monitoring of flaps |
| topic | photoplethysmography deep learning flap |
| url | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.230624 |
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