Department of Surgery, Section of Neurosurgery, The Aga Khan University, Karachi, Pakistan.
J Pak Med Assoc. 2024 Apr;74(4 (Supple-4)):S158-S160. doi: 10.47391/JPMA.AKU-9S-24.
Image learning involves using artificial intelligence (AI) to analyse radiological images. Various machine and deeplearning- based techniques have been employed to process images and extract relevant features. These can later be used to detect tumours early and predict their survival based on their grading and classification. Radiomics is now also used to predict genetic mutations and differentiate between tumour progression and treatment-related side effects. These were once completely dependent on invasive procedures like biopsy and histopathology. The use and feasibility of these techniques are now widely being explored in neurooncology to devise more accurate management plans and limit morbidity and mortality. Hence, the future of oncology lies in the exploration of AI-based image learning techniques, which can be applied to formulate management plans based on less invasive diagnostic techniques, earlier detection of tumours, and prediction of prognosis based on radiomic features. In this review, we discuss some of these applications of image learning in current medical dynamics.
图像学习涉及使用人工智能 (AI) 来分析放射学图像。已经采用了各种基于机器和深度学习的技术来处理图像并提取相关特征。这些特征可以用于早期发现肿瘤，并根据其分级和分类来预测其生存率。放射组学现在也用于预测基因突变，并区分肿瘤进展和与治疗相关的副作用。这些曾经完全依赖于活检和组织病理学等有创性程序。现在，这些技术的使用和可行性在神经肿瘤学中得到了广泛探索，以制定更精确的管理计划，限制发病率和死亡率。因此，肿瘤学的未来在于探索基于人工智能的图像学习技术，这些技术可以应用于制定管理计划，这些计划基于侵袭性较小的诊断技术，更早地发现肿瘤，并根据放射组学特征预测预后。在这篇综述中，我们讨论了当前医学动态中图像学习的一些应用。
