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The secret weapon for early diagnosis of lung cancer: How does CAD improve detection accuracy?
With the rapid development of medical technology, computer-aided diagnosis (CAD) is becoming an important tool for early diagnosis of lung cancer. With the development of imaging medical technologies such as X-ray, MRI, endoscopy and ultrasound, radiologists are faced with the challenge of analyzing large amounts of imaging data. The purpose of these technologies is to detect possible lesions, especially lung cancer, early, allowing medical professionals to make accurate judgments in a short period of time.
CAD systems process digital images or films to highlight significant areas and possible disease symptoms to support professionals in making decisions.
These systems help doctors identify potential health problems in an automated way, especially when examining lung CT images, where CAD can effectively mark areas that require special attention. For lung cancer patients, early detection is crucial because the effectiveness of treatment is closely related to early detection.
Since the advent of computer technology in the 1950s, many researchers have begun to explore the possibility of building CAD systems. Early CAD systems were often called "expert systems" and they used statistical pattern matching and probability theory to drive the decision-making process. Over time, however, researchers discovered the limitations of these systems and began looking for more advanced solutions.
CAD systems have been used in clinical settings for more than 40 years, and while these technologies have never replaced the role of the physician, they have certainly provided valuable support to the physician.
With the popularization of digital images and the continuous advancement of AI and computer vision technologies, the performance of CAD systems has gradually improved. The basic principle of these systems is high-complexity pattern recognition, which helps doctors analyze different structures in images through a series of preprocessing and segmentation algorithms. During the evaluation process, each examined area is classified and scored according to specific features, and the system then marks possible abnormal areas and provides them to radiologists for further interpretation.
CAD systems have demonstrated their potential in many applications by improving the sensitivity and specificity of inspections. For example, in mammography, CAD can highlight clusters of microcalcifications and dense structures, which may indicate the presence of cancer. As a result, CAD has become a powerful assistant for radiologists, helping them make critical decisions faster.
Today's CAD systems cannot detect 100% of pathological changes, but their hit rates can be as high as 90%.
However, CAD systems also face many challenges, and current technology cannot completely replace experienced radiologists. Even with improvements in sensitivity and specificity, the physician remains responsible for the final interpretation of the images. Many studies have shown that the false-positive rate of CAD is high, which may lead to unnecessary anxiety and additional examinations for patients. Therefore, how to strike a balance between improving detection accuracy and controlling the false positive rate remains an important issue.
In the diagnosis of lung cancer, CAD can mark circular lesions smaller than 30 mm in chest images, increasing the chances of early detection. In addition, its application in emergency diagnostic imaging is gaining increasing attention, providing timely information for a considerable number of critical situations.
As machine learning and deep learning techniques advance, the accuracy of CAD systems continues to improve. Currently, many CAD systems use AI to analyze and interpret images, which not only reduces the risk of misunderstanding but also speeds up diagnosis. These systems have achieved considerable success in the screening of various types of cancer, including lung cancer, breast cancer, colon cancer, etc.
From the past to the present, the advancement of CAD systems has shown its increasingly important role in clinical diagnosis.
In the future, with further development of technology and integration of medical data, computer-assisted diagnosis has the potential to become a more reliable diagnostic tool. But we must also think about how we can better use these tools to improve the existing medical system in the face of booming technology?
