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A Narrative Review Of Machine Learning Systems In The Diagnosis Of CT Scan
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چکیده: (2105 مشاهده) |
| Clinicians rely on their knowledge and experience, as well as the results of complex and time-consuming clinical trials, despite the inevitable human error, to diagnose and treat diseases. The application of machine learning sheds light on the ability of these techniques to help correctly diagnose some diseases. Nowadays, with the emergence of the COVID-19 pandemic, the mass number of COVID-19 cases who refer to medical centers increased through the last two years of the pandemic, leading to a large number of patient records being collected in the medical database; while these resources not being used properly in some cases. In this regard, data mining and machine learning approaches have attracted researchers to help to predict and diagnosing disease. Through the literature, machine learning is applied to extract patients' characteristics and their correct classification to help diagnose suspicious cases. Due to the need for sufficient experience to diagnose the COVID-19 based on the imaging methods of the lung, researchers have thought of using intelligent diagnostic methods by modeling the knowledge of skilled radiologists using machine learning algorithms. Our study showed how machine learning-based analysis of lung imaging can lighten the burden on radiologists, as they must review and prioritize the growing number of imaging findings in a pandemic peak. Also, there is a need for big datasets for better training of Machine learning algorithms; while literature review shows the possibility of statistical errors like duplication of images in public imagining repositories of COVID-19. But yet it is unlikely that these machine learning algorithms will be used instead of standard nucleic acid tests as the primary tool for detecting COVID-19; while broad application of these inexpensive systems could occasion opportunistic screening of COVID-19 in lung scans. |
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نوع مطالعه: مروری |
موضوع مقاله:
عمومى
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