Alyami Jaber
Department of Radiological Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
King Fahd Medical Research Center, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
EJNMMI Rep. 2024 Apr 1;8(1):7. doi: 10.1186/s41824-024-00195-8.
Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.
利用机器学习进行放射图像分析已被广泛应用，以提高活检诊断的准确性，并协助放射科医生进行精确治疗。随着医疗行业及其技术的进步，计算机辅助诊断（CAD）系统对于检测患者早期无法通过肉眼观察到的癌症迹象至关重要，且不会引入错误。CAD是一种通过计算机视觉将人工智能技术与图像处理应用相结合的检测系统。目前已有几种用于癌症诊断的手动程序。然而，它们成本高昂、耗时且只能在癌症晚期进行诊断，如CT扫描、X光摄影和MRI扫描。在本研究中，评估了多种使用临床实践进行多器官检测的先进方法，如癌症、神经、精神、心血管和腹部成像。此外，将众多合理的方法聚类在一起，并在基准数据集上评估和比较它们的结果。采用准确率、灵敏度、特异性和假阳性率等标准指标来检验文献中报道的当前模型的有效性。最后，突出了现有问题，并提出了未来工作的可能方向。
