| Title | Machine learning-supported autofluorescence spectral analysis for detecting urinary stone composition in emulated intraoperative ambient |
|---|---|
| Description | The prevalence and disease burden of urolithiasis has increased substantially worldwide in the last decade, and intraluminal holmium laser lithotripsy has become the primary treatment method. However, inappropriate laser energy settings increase the risk of perioperative complications, largely due to the lack of intraoperative information on the stone composition, which determine the stone melting point. To address this issue, we developed a fiber-based fluorescence spectrometry method that detects and classifies the autofluorescence spectral signals of urinary stones into three categories: calcium oxalate, uric acid, and struvite. |
| Organism | Homo sapiens |
| Data Type | Other Type of Clinical information |
| Data Accessibility | Controlled-access |
| BioProject | PRJCA013902 |
| Release Date | 2024-12-16 |
| Submitter | An Yan (741234043@qq.com) |
| Organization | Southwest Hospital of the Third Military Medical University |
| Submission Date | 2022-12-15 |
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| File ID | File Title | Number/Samples | File Type | File Size | File Suffix | Download |
|---|---|---|---|---|---|---|
| OMIX002610-02 | Analysis of stone composition in patients with stone | 117 | Other Type of Clinical information | 12.4 KB | xlsx | Controlled |
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