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.
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.
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.
Source
Compassionate Artificial Superintelligene AI-5.0, Dr. Amit Ray, 2018
References:
- 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
- How Artificial Intelligence Will Change Medical Imaging Dave Fornell, 2017
- Artificial intelligence in healthcare: past, present and future, BMJ, 2017