Artificial Intelligence in Radiology:
Use cases and best practices
Doctors and healthcare professionals have limited capability of examining and processing information regarding any disease. The healthcare sector is becoming digital and well-organized thanks to Artificial Intelligence. The vital impact has been seen of AI in the radiology sector of healthcare.
The field of computer vision is expanding quickly and is easy to transform healthcare. The technique combines optical sensors & cameras with potent artificial intelligence algorithms. Artificial Intelligence in radiology has progressed due to significant improvements in processing power and improved data access.
In this blog, we bring to light in detail the use cases of AI in the arena of radiology and also talk about their benefits. We will begin with a quick introduction to AI in radiology.
What do you mean by AI in Radiology?
Artificial intelligence is growing efficiently in the sector of medical science, especially in radiology. In radiology, it’s utilized to detect illnesses starting at an early stage, permit long-term treatment planning, and provide the greatest accuracy possible. AI helps doctors in making accurate clinical decisions and management of datasets. Using this technology, diagnostic imaging solutions can be analyzed to determine the best treatment for chronic abnormalities in human as well as animal bodies.
Artificial intelligence development companies in India are providing AI-based services related to radiology in a wide range as it has been proved that AI detects and diagnoses illness more accurately as compared to doctors and this is the reason it is adopted by health centers in radiology sectors a wide range across the globe.
AI companies are providing enterprise development solutions that are hitting the market of radiology. The market for AI radiology, which was estimated to be worth $55.7 million in 2021, is anticipated to increase at a CAGR of 36.7% to become $517.8 million by 2030.
Top 10 AI use cases:
1. Breast cancer detection
Breast cancer is becoming a popular cause of death for women globally. The main features of AI used in breast cancer imaging are Machine learning and deep learning. AI is mainly used in breast cancer screening which involves the detection of disease, and classification of the tumor.
Mammography is now used to detect cancer through screening but sometimes doctors are not able to analyze cancer at an early stage with mammograms. AI algorithms are not developed completely yet but current machine learning models are being made by Artificial Intelligence developers with inbuilt medical image information that provides better analysis and more accurate detection of cancer.
2. Detecting hidden fractures
According to a Radiology study, artificial intelligence (AI) is a useful tool for fracture identification that has the potential to help physicians in emergencies. A frequent error that could have severe effects on the patient due to mistaken or late detection of fractures on X-rays is eliminated due to AI in radiology.
AI helps in detecting fractures that are difficult to diagnose due to being covered in multiple tissues like hip fractures which are common in old age.
3. Enhancing cardiac imaging
Artificial intelligence (AI) has the power to enhance patient care at every point of the imaging process with the availability and usage of massive datasets. According to recent research, automatic identification of cardiac structures can, in the near term, speed up the diagnosis process, minimize human errors, and save time.
There are multiple applications for cardio like AI that analyzes irregular heartbeats, and AI applications for better heart visibility it colors the heart chambers for better understanding for doctors. Anyone willing to build an AI app for cardiac imaging can hire mobile app developers in India to assist them in their initiative toward healthcare.
4. Detecting neurological disorders
Diseases associated with the peripheral and central nervous systems are known as neurological disorders. Muscle weakness, loss of consciousness, paralysis, etc are typical symptoms of neurological disorders. Neurologists, neurosurgeons, radiologists, & other healthcare professionals can benefit from automated artificial intelligence (AI)/machine learning approaches for making better clinical judgments. By analyzing physiological symptoms and images AI can detect neurological disorders like epilepsy, Parkinson’s disease, Alzheimer’s disease, brain stroke, etc.
5. Classifying Brain Tumors
AI-based deep learning is trending in the research sector these days. Convolutional Neural Network (CNN), the most popular deep learning architecture, was utilized to classify the dataset of brain MR images into three categories (Glioma, Meningioma, and Pituitary Tumor). The suggested CNN classifier is an effective technique, it performs well overall in terms of accuracy. AI can be incorporated with multiple imaging methods to create computer-aided diagnostic (CAD) systems, which can be used for tumor detection in the realm of computer vision applications.
6. Radiomics
A vast number of quantitative features from medical images are intended to be extracted utilizing data characterization methods in the field of medicine known as Radiomics. The information is evaluated for better decision aid. It may reveal disease-related traits that are challenging to spot with human eyesight.
AI in radiology can help analyze data by making use of patients’ signatures, it can also be utilized in correlating radionics and other data like proteomics, genomics, etc.
7. Delivers a second opinion
When radiologists contradict a worrisome medical image, AI systems can operate in the backdrop and provide a second opinion. This activity helps radiologists learn how to work with AI and reduces the stress of making decisions while making decisions whilst still enabling them to see its benefits.
The first organization to discover coronavirus ‘Mount Sinai Health Department’ used AI as a second opinion while detecting COVID-19.
8. Detection of Heart Failure
By accurately identifying persons at an elevated risk of cardiac instability over traditional risk levels, artificial intelligence (AI) systems can contribute to reducing the number of individuals who inadvertently require treatment because of cardiac failure. The application of machine learning algorithms to the area of heart transplantation is another crucial topic. Such AI algorithms are employed to forecast whether a patient on the waiting list will pass away or receive a lifesaving transplant.
9. Dose optimization
Overdosage can be injurious, especially for children. It is very important to optimize dose While choosing a customized patient treatment, monitoring the patient’s dosage ratios, and calculating the radiation risks related to dosage also the patient’s susceptibility, technicians and radiologists can benefit from the use of Artificial Intelligence in radiology. The dose-optimization method entails adjusting a variety of visibility factors in order to find those that provide images with the least radiation dosage while still being able to meet the bare minimum of diagnostic needs.
10. Imaging biobanks
Plenty of information can be saved as the memory capacity of machines is always expanding. PACS (picture archiving and communication system) overflow in radiology is primarily caused by the necessity to retain native pictures and huge data from statistical imaging. In big imaging biobanks, quantitative imaging can create imaging biomarkers that are able to be processed, evaluated, and utilized to forecast the risk of disease in massive population studies and the outcome of therapy.
Benefits of AI in Radiology
- More accurate classification
Artificial intelligence radiological tools are getting advanced. It can now detect even a minor abnormality which human radiologists also sometimes fail to catch. This results in better treatment and quick recovery of patients. - Improved image analysis
It enhances the medical images resulting in better detection and helps in analyzing and making decisions regarding the analysis. Doctors make a confident diagnosis through the second opinion provided by AI in radiology. - Early virus detection
AI technology detects severe diseases like cancer at an early stage and helps in treating patients quickly. It can save more lives as compared to before times when cancer was a life-threatening disease having no detection and cure. Those days are gone, now it can be easily detected at an early age and patients can live for years after treatment. - Reporting
Artificial Intelligence in radiology has eliminated analysis and report preparation for doctors saving their time and effort. It creates flawless and sequential reports that simplify the workflow. - Enhanced prioritization
AI in radiology is becoming more and more intelligent day by day. It can distinguish between serious patients who need to be urgently hospitalized and patients who are not extremely sick. Patients can be given priority according to their severity.
Final Thoughts
AI in radiology is skyrocketing for its advancement in tools and for assisting radiologists in examining and diagnosing diseases. Artificial Intelligence companies in India are providing a pool of AI tools to healthcare care providers helping them at every step of diagnosis. Radiologists will be well served by continuously evaluating the benefit of these tools in their practices because they are fully responsible for the treatment of the patient, not the technology.
AI developers have created technologies that allow all aspects of radiology. It provides everything from data collection to storage and also aids in decision-making with second opinions.
FAQs
What applications of AI are currently being used in radiology?
AI is currently concentrating on enhancing diagnostic capability and supporting radiologists in handling acute illnesses like cerebral hemorrhage, pulmonary embolism, and pneumothorax. The outcome of the patient is significantly impacted by the ability to evaluate these problems in a matter of time.
Which are the top 5 applications popularly used in AI?
Many applications are in use in radiology that are AI-based. 5 most used ones are mentioned below:
- Detecting breast cancer
- Dose optimization
- Detecting pneumonia
- Spotting vertebral fractures
- Detecting Alzheimer’s disease.
How will AI impact radiology in the future?
Artificial intelligence and its impact on the future of healthcare are the subjects of much hype and worry. There are several indications that artificial intelligence will fundamentally alter the medical industry. Many radiologists are worried as deep learning algorithms and narrow A.I. began to gain popularity, particularly in the realm of medical imaging.
