# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 2 Applications of Deep Learning Approaches for Diagnostic Classification
Deep learning models applied to cryo-electron microscopy (cryo-EM) data were able to quantitatively predict protein dynamics properties with higher accuracy than conventional methods, demonstrating the ability to extract subtle patterns from imaging data [58]. An automated deep learning method for segmenting and quantifying coronary atherosclerosis from CT angiography showed promise for providing consistent, quantitative measurements to guide clinical decision-making [59; 55; 60; 61; 62]. While deep learning approaches offer significant advantages in terms of consistency and quantification, it is important to note that the performance depends on the quality of the training data and model design. Ongoing validation against expert assessments remains important. However, the evidence suggests deep learning can reduce inter-observer variability while providing detailed quantitative insights from medical imaging data [63; 64]. AI in radiology is indeed revolutionizing diagnostic capabilities by identifying subtle imaging features that may be overlooked in traditional radiological practice [65]. This enhancement in diagnostic precision is transforming the field of medical imaging and improving patient care. AI algorithms, particularly those utilizing deep learning, excel at detecting minute patterns and anomalies in medical images that might escape human detection [64]. These systems are trained on vast datasets of radiological images, allowing them to recognize subtle indicators of various conditions with remarkable accuracy, such as early-stage cancers, brain aneurysms, and cardiac abnormalities. By identifying these subtle features, AI enables earlier and more accurate diagnoses, which can significantly impact patient outcomes and treatment success rates [66]. The integration of AI into radiology workflows has led to significant improvements in both efficiency and diagnostic accuracy [48; 65; 67]. AI systems can rapidly analyze large volumes of imaging data, prioritizing cases that require urgent attention [65; 68]. They provide consistent and precise image analysis, reducing variability among radiologists and ensuring more uniform interpretations. AI algorithms can detect abnormalities in imaging scans with a level of detail and speed that often surpasses human capability [69; 70]. This enhanced efficiency allows radiologists to focus their expertise on complex cases, ultimately improving the overall quality of patient care. The synergy between AI and advanced imaging technologies is opening new frontiers in radiological assessment [65]. AI combined with 3D imaging technology has significantly improved image clarity and detail, allowing for more nuanced interpretations of complex anatomical structures [69]. Integration with molecular imaging facilitates detailed analyses of biological processes at the cellular and molecular levels, contributing to earlier and more accurate diagnoses [46; 70; 71]. One of the most powerful aspects of AI in radiology is its ability to continuously learn and improve [72; 73]. AI algorithms engage in iterative learning processes, constantly refining their capabilities as they are exposed to new data [74]. This ongoing improvement ensures that diagnostic tools become increasingly reliable and robust over time. As AI systems evolve, they have the potential to uncover new insights from imaging scans that might have been previously overlooked, further advancing early disease detection and treatment. The enhanced diagnostic capabilities of AI are making significant impacts across various medical specialties [75]. In hepatology and pancreatology, AI has improved the diagnosis of liver and pancreatic diseases using various imaging techniques, including ultrasound, CT, MRI, and Positron Emission Tomography (PET)/CT [76; 77]. For abdominal and pelvic imaging, AI facilitates automated segmentation and registration of organs and lesions, increasing diagnostic accuracy and treatment efficacy [78; 79; 80; 81; 82; 83].