| Technical Name | Automatic microscopy system for Mycobacterium tuberculosis identification by using artificial intelligence deep learning | ||
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| Project Operator | National Cheng Kung University | ||
| Project Host | 孫永年 | ||
| Summary | Acid-fast bacilli sputum microscopy is one of the most effective detection methods for tuberculosis in clinical practice. The system is comprised of high-speed automatic focusing image acquisition and artificial intelligence deep learning tuberculosis identification technologies; the accuracy is over 94%, which can specifically meet the urgent needs in clinical microscopy examination. |
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| Scientific Breakthrough | The system acquires more than 600 fields of view per smear, which is twice as many as manual microscopy. Generally, the accuracy of manual sputum microscopy is about 30%~70%, and the system can be as high as 90%~97%. The system takes about five minutes to process a smear, which saves 75% of time when compared with manual checking. After applying this system, specialists can work more efficiently. |
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| Industrial Applicability | The system is compatible with the microscopes currently used in medical institutions and yields economic benefits. In high tuberculosis burden countries, the system provides a higher quality and efficient inspection platform to improve the screening accuracy of tuberculosis. This gives relatively early diagnosis and appropriate treatment for the patients, and prevents the disease from spreading. |
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| Keyword | Tuberculosis sputum microscopy automatic microscopy image acquisition automatic M. tuberculosis identification artificial intelligence deep learning Mycobacterium tuberculosis CNN microscope identification | ||