AI-Powered Radiology: The Next Frontier in Medical Imaging
Advanced medical imaging technologies, such as computed tomography (CT) scans, X-Rays, and magnetic resonance imaging (MRI) scans, have revolutionized the way doctors diagnose and treat diseases.
However, interpreting medical images is a time-consuming and complex process that requires a high level of expertise.
In recent years, integrating artificial intelligence (AI) in radiology has opened up new possibilities in medical imaging.
In this blog, we will explore how AI-powered radiology is the next frontier in medical imaging and what are its applications.
What is AI-powered radiology?
AI-powered radiology integrates artificial intelligence in radiology to improve the accuracy and efficiency of medical image analysis.
This technology uses machine learning algorithms trained on large medical image datasets to identify patterns and anomalies.
AI-powered radiology systems can help radiologists reduce their workload and improve accuracy by minimizing false positives, negatives, or missed detections.
Furthermore, these AI-powered radiology systems can assist radiologists in providing personalized treatment plans.
Applications of AI-Powered Radiology
1. Lung Cancer Detection
AI-powered medical imaging tools can identify subtle signs and abnormalities that are difficult to detect through traditional radiological methods.
This enhanced detection capability can lead to early diagnosis, improving the survival rate among lung cancer patients.
AI-powered detection systems in radiology can be useful in reducing the workload and improving the efficiency of the following lung cancer diagnosis processes:
• Low-dose computed tomography (LDCT)
• Chest radiographs (CXR)
Using AI in lung cancer diagnosis processes can improve the accuracy and efficiency of the overall diagnosis process.
AI-powered algorithms can analyze medical images, assist in pathology slide reading, and detect nodules, all of which can aid doctors in the early detection and treatment of lung cancer.
Moreover, many multinational tech firms are developing their own versions of AI-powered radiology systems.
For instance, on May 20, 2019, Google announced the development of an AI-powered system that can detect lung cancer in medical images with high accuracy.
The findings were published in Nature medicine.
Using advancements in 3D volumetric modeling and datasets from its partners, including Northwestern University, Google created an AI model that can generate lung cancer malignancy predictions in 3D volume and identify even the subtlest malignant tissues in the lungs, known as lung nodules.
To validate these results, Google used 45,856 de-identified chest CT screening cases from the national lung screening trial study issued by the National Institutes of Health (NIH) and Northwestern University.
Some of these screening cases had cancer. To prove the efficiency of its AI model, Google also compared its results against six U.S. board-certified radiologists.
The results showed that Google’s AI model performed better than the six radiologists while using a single CT scan diagnosis.
The model detected 5% more cancer cases while reducing false-positive exams by over 11% compared to unassisted radiologists.
Furthermore, AI models such as these can help revolutionize the way we diagnose and treat cancer, leading to increased survival rates.
2. Breast Cancer Detection
In recent years, there has been a surge in the use of AI-powered radiology systems for detecting breast cancer, the most common cancer affecting women worldwide.
Mammography is the widely used screening tool for breast cancer detection, but it has been found that approximately 40% of cancers cases are missed by doctors while evaluating mammograms.
With the help of AI and computer vision technology, medical imaging systems can accurately analyze mammograms to detect cancers that may not be visible to the human eye.
Machine learning models are trained with medical image data to provide a more accurate analysis of breast masses, breast density, and mass segmentation, providing better cancer risk assessments.
For instance, with the help of machine learning algorithms, AI is also being used to identify metastatic breast cancer in whole slide images of lymph node biopsies.
AI-powered radiology systems can reduce the workload of radiologists by flagging areas that require further investigation, providing more accurate diagnoses.
This approach analyzed data obtained from radiomics and biopsy slides using AI algorithms to identify subtle signs of breast cancer.
Furthermore, this can reduce the number of false positives, which can prevent unnecessary biopsies and anxiety for patients and can help doctors to develop an effective treatment plan for patients.
With significant research and investment, AI-based algorithms can also play a critical role in identifying potential targets for immunotherapy, such as neoantigens, which are proteins only present on the surface of cancer cells.
By analyzing large amounts of patient data, including genomic data, AI algorithms can identify potential neoantigens that can be targeted by immunotherapy drugs.
Furthermore, AI algorithms can also help predict a patient’s response to immunotherapy by analyzing radiomic characteristics in medical images, such as tumor size, shape, and texture.
3. Tumor Detection and Classification
Identifying and classifying the type of tumor is crucial in diagnosing and treating cancer. However, it is a time-intensive process.
AI-powered medical imaging tools can help expedite the process and accurately detect tumors and other abnormal structures in medical images such as MRI and CT scans.
A study published in 2020 shows that AI, combined with advanced imaging technology called stimulated Raman histology (SRH), can improve intraoperative pathology practice.
Dr. Daniel Orringer, M.D. of NYU Langone Health, and Dr. Todd Hollon, M.D., a chief neurosurgery resident at the University of Michigan, led the research team.
Results showed that this integrated technology could classify tumors in less than three minutes, three times faster than manual methods, leading to faster and more precise diagnoses.
In the U.K., another study was published in 2021 on research conducted by a team of experts from the University of Birmingham, including researchers from Warwick Manufacturing Group (WMG), University of Warwick.
It was found that AI and advanced imaging effectively diagnose brain tumors in children without invasive procedures.
The researchers used advanced imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) to obtain high-resolution brain images.
AI algorithms were used next to analyze the images and identify patterns that could distinguish diffuse midline gliomas (DMGs) from other brain tumors.
Furthermore, accurately classifying DMGs could lead to more personalized treatment options and improved outcomes for children with this type of brain tumor.
SmartXR: AI in Digital Radiology
SmartXR AI is developed by Agfa-Gevaert Group, based in Belgium, and develops, produces, and distributes an extensive range of imaging systems and IT solutions.
It offers four AI-integrated radiology solutions that are as follows:
Smart Positioning- It provides a live view of the patient’s position from the tube’s perspective through digital overlays produced through augmented reality.
Color-coded indications are given as warnings when the patient is not within the active X-Ray area or exposure.
This reduces retakes due to positioning, and exposure can be applied to the particular area of study.
Smart Align- It uses advanced sensing to provide real-time information on the precision of the alignment between the tube and panel.
Smart Dose- It uses 3D machine vision, which suggests personalized dosage for a patient by measuring the thickness of the region of interest.
Smart Rotate- It uses a deep neural network to automatically rotate X-Ray images as required and interpret the image contents.
Technology like SmartXR can aid in reducing the burden of busy hospitals by allocating half of the work to AI, saving cost and money leading to better patient care.
AI-based technologies are expected to bring significant growth in the medical X-Ray detector market.
According to BIS Research, the global medical X-Ray detectors market was valued at $2.01 billion in 2021 and is expected to reach $3.70 billion by the end of 2032, growing at a CAGR of 5.50% during the forecast period from 2022 to 2032.
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Conclusion
With the ability to automate processes, accurate diagnoses, and personalized treatment plans, AI-powered radiology systems are poised to revolutionize medical imaging and improve patient outcomes.
However, it is essential to recognize that AI-powered radiology is not a replacement for human expertise but rather a tool to enhance and complement existing medical imaging processes.
The future of AI-powered radiology in precision medicine and digital healthcare is exciting, and we can expect many more advancements in this field in the future.
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