Artificial Intelligence in Radiology for X-Ray and CT-Scan

Artificial Intelligence in Radiology for X-Ray and CT-Scan Image Analysis 

Dr. Amit Ray
Compassionate AI Lab, Radiology Division

With artificial intelligence it is possible to analyze and interpret large amounts of radiological images efficiently. Late detection of disease significantly increases treatment costs and reduces survival rates. Often visual human interpretation of an isolated image are time-consuming, difficult and expensive. Artificial intelligence in healthcare especially in radiology has tremendous scope. Recently, AI for radiology uses deep learning, reinforcement learning, and other machine learning algorithms to systematically assess x-ray, CT Scan and MRI images of and instantly provide detailed reports on their findings.

AI algorithms read medical images like a radiologist, they identify the hidden patterns in the image and relate them with medical data. The AI systems are trained using vast numbers of images like CT scans, magnetic resonance imaging (MRI), ultrasound or nuclear imaging. New AI tools that excel at medical image analysis can automatically detect complex anomalous patterns in radiological images and provide quantitative information on disease [4].

Studies have shown that computer aided screening can decrease false negatives by ~45% [1]. The AI machines reading radiology studies correctly, reaching around 95 percent accuracy [2]. The AI tools can rapidly review a number of images, prior images, patient history and other medical data, and then extract the most meaningful insights, which can then be verified by the radiologist.

Machine Learning Algorithms for Radiology Analysis

The primary machine learning algorithms used for AI based radiological systems are include linear regression, logistic regression, naive Bayes, decision tree, nearest neighbour, random forest, discriminant analysis, support vector machine (SVM) and neural network. However, among these support vector machine (SVM) and neural network are most popular and effective[3]. Currently, deep learning and deep reinforcement algorithms are used for analysis and interpretation of radiological images like CT scan, MRI and nuclear images.  For more information on deep learning you can read Deep Learning Past Present and Future – A Review.

Machine learning provides an effective way to automate the analysis, interpretation and diagnosis for medical images. It can potentially reduce the load on radiologists in the practice of radiology. Machine learning techniques they can be categorized into supervised learning, unsupervised learning, and reinforcement learning algorithms. 

In supervised learning, each sample contains two components: one is input images, observations or features and the other is output observations or labels. These input and output data are to train the AI systems. Support vector machines (SVMs) are a set of kernel-based supervised learning methods used for classification and regression.

Convolutional Neural Networks (CNN) for Radiology

We have used the popular deep convolutional neural networks (CNN) for AI based automated X-Ray and CT-Scan image analysis,  diagnosis and measurements. We have used 21 different convolutional neural networks (CNN) of five different architectures (ResNet, DenseNet, VGG, SqueezeNet and AlexNet) for mobile x-ray device and automated diagnosis. The key architecture are AlexNet, Inception V3, Xception, ShuffleNet, Alexnet-152, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNet-179, SqueezeNet 1.0, VGG-16, VGG-19, DenseNet-40, DenseNet-50, DenseNet-121, DenseNet-161, DenseNet-169, DenseNet-201, DenseNet-219. 

The 21 Key CNN Architectures used in Sri Amit Ray Compassionate AI Lab for Radiology

In unsupervised learning there is only the input observations. The algorithms find the patterns in the data. Examples of unsupervised learning include clustering, density estimation, and blind source separation. 

Reinforcement learning (RL) is a goal-oriented learning based on interaction with environment.   In reinforcement learning the machine or software agents learn its behaviour based on feedback from the environment and maximize its long-term reward. Reinforcement learning could be used in radiology for medical image segmentation to incorporate the knowledge gained from the new patients into the computer-aided detection and diagnosis (CAD) systems. However, AI is going to play a key role in radiology applications to identify complex patterns in diverse types of radiology data.

Applications of AI in Radiology

The applications of AI in radiology includes:

1. Image Interpretation and Analysis: AI excels at image analysis, allowing it to detect subtle abnormalities and anomalies that might go unnoticed by the human eye. It can aid in the early detection of diseases like cancer, cardiovascular conditions, and neurological disorders, significantly improving patient outcomes.
2. Diagnostic Assistance: AI-powered diagnostic tools can provide radiologists with valuable insights and recommendations, helping them make more accurate and informed decisions. This collaboration between AI and human experts creates a synergistic approach that leverages the strengths of both.
3. Quantitative Analysis: AI can extract quantitative data from images, enabling more precise measurements of structures and tissues. This has proven crucial in tracking disease progression, assessing treatment efficacy, and predicting patient outcomes.
4. Workflow Optimization: AI algorithms can streamline the radiology workflow by automating routine tasks such as image preprocessing, sorting, and prioritization. This leads to faster turnaround times and increased efficiency in patient care.
5. Predictive Analytics: By analyzing historical data, AI can predict disease patterns, patient risk factors, and potential complications. These insights contribute to personalized treatment plans and proactive healthcare management.
6. Educational Tools: AI-powered platforms can serve as educational tools, offering training modules, simulations, and case-based learning to support the continuous professional development of radiologists.

Will Artificial Intelligence Replace Radiologists?

AI is not the harbinger of radiologists’ obsolescence; it is a potent tool in their diagnostic arsenal. By assisting in spotting subtle anomalies, quantifying measurements, and prioritizing cases, AI can augment radiologists’ capabilities and enable them to focus on the intricacies of patient care that machines cannot comprehend. This partnership empowers radiologists to leverage AI as a force multiplier, rather than being pushed aside by it. While AI is undoubtedly transforming the field of radiology, the current consensus among experts is that AI is more likely to augment and enhance the role of radiologists rather than replace them entirely. Here’s why:

1. Continual Learning and Adaptation: Radiologists undergo years of extensive training and continuous education to stay updated on medical advancements and imaging techniques. AI algorithms require large amounts of data for training and may not adapt well to rapidly changing medical knowledge. Radiologists bring a depth of medical understanding that allows them to critically evaluate evolving research and adapt their practices accordingly.
2. Ethical and Legal Considerations: AI in healthcare raises ethical and legal questions, particularly in terms of accountability, patient privacy, and liability. The responsibility for patient care and outcomes ultimately rests with human healthcare professionals. Radiologists are accountable for the accuracy and reliability of their diagnoses, and AI should be viewed as a complementary tool rather than a substitute.
3. Patient Interaction and Communication: Radiologists play a crucial role in communicating findings and treatment recommendations to patients and other healthcare providers. The human touch, empathy, and communication skills that radiologists possess are essential in building trust and providing personalized care to patients.
4. Diverse Modalities and Clinical Nuances: Radiology spans a myriad of imaging modalities and intricate clinical contexts. AI algorithms often specialize in distinct tasks, such as identifying fractures or detecting tumors on specific types of scans. However, radiologists possess the expertise to interpret a wide array of images and correlate findings with the patient’s holistic medical history—a feat AI struggles to achieve. Radiologists’ ability to adapt to diverse scenarios sets them apart as indispensable medical professionals.

Challenges and Future Directions

While the integration of AI in radiology holds immense promise, several challenges must be addressed. Data privacy and security concerns, regulatory compliance, and the need for rigorous validation of AI algorithms are critical considerations. Ensuring that AI systems are transparent, interpretable, and accountable is paramount to gaining the trust of healthcare professionals and patients alike.

Looking ahead, the future of AI in radiology seems bright. As AI models continue to learn from vast datasets and gain experience, their diagnostic accuracy is likely to improve further. Combining AI with other emerging technologies such as 5G networks and edge computing can enhance real-time image analysis and remote consultations, extending quality healthcare to underserved areas.

Conclusion:

The main advantages of applying machine learning in radiology will be saving of professional times and accurate diagnostic results. Many AI based image segmentation systems in radiology systems have shown comparable, or even higher, performance compared with well-trained and experienced radiologists and technologists. The critical component of machine learning is getting reliable data to train and test the accuracy of the systems. 

While AI is revolutionizing radiology by improving efficiency, accuracy, and diagnostic capabilities, it is unlikely to fully replace radiologists. The future of radiology lies in a collaborative partnership between AI and human experts, where AI supports radiologists by automating routine tasks, aiding in image analysis, and offering valuable insights. This symbiotic relationship is poised to reshape the field, leading to more accurate and timely diagnoses and ultimately improving patient outcomes.

Source

Compassionate Artificial Superintelligene AI-5.0, Dr. Amit Ray, 2018

References:

1. Evaluation of a Real-time Interactive Pulmonary Nodule Analysis System on Chest Digital Radiographic Images van Beek, Edwin J.R. et al. Academic Radiology , Volume 15 , Issue 5 , 571 – 575 
2. How Artificial Intelligence Will Change Medical Imaging Dave Fornell, 2017
3. Artificial intelligence in healthcare: past, present and future, BMJ, 2017