Key Takeaways
- With artificial intelligence imaging, the issue of image retake can be eliminated as AI helps to boost the quality of images. AI also enhances low-quality scans by decreasing artifacts and noise.
- AI algorithm can be trained to spot abnormalities in medical imaging. According to a study, an AI model was developed to differentiate between normal and abnormal CXRs (Chest X-rays) across multiple settings and de-identified datasets.
- Use of AI for screening helps to detect chronic diseases such as cancer, cardiovascular diseases, and lung diseases at an early stage. A study pointed out that AI can accurately diagnose and detect colorectal cancer from tissue scans better than pathologists.
AI in medical imaging software plays a crucial role in enhancing patient care and providing quick body analysis. So, researchers and software developers are looking for ways to effectively incorporate artificial intelligence imaging.
With AI in medical imaging software, radiologists and physicians will have no difficulty in spotting abnormalities or improving precision medicine. They can also track patient health effectively and detect body issues as early as possible. However, there is a need for many more radiologists to proactively make use of AI imaging technology. According to a report published by the American College of Radiology in 2021, only 30 percent of radiologists use AI clinically in current practice. Considering the data, there is tremendous scope for adopting artificial intelligence in imaging.
Key Features of AI in Medical Imaging Software
1. High-Quality Image
A medical image is considered of good quality only when certain criteria are satisfied and it has a diagnostic value. A low-quality image has a higher chance of rejection and procedures may be repeated to produce a quality image. Image retakes not only cost more money but also take ample time to complete the diagnostic procedure.
With artificial intelligence imaging, the issue of image retake can be eliminated as it helps to boost the quality of images. AI also enhances low-quality scans by decreasing artifacts and noise and boosting contrast, thereby giving providers a clear picture of a patient’s body. Artificial intelligence decreases scanning time, avoids contrast agents, and lowers radiation dosage.
2. Image Registration
In image registration, data from multiple images are pulled together to form a geometric alignment. This is done in either of two ways: 4D fusion or image fusion.
AI-driven image registration ensures treatment simulation, motion management, and radiotherapy. Without AI, the process of image registration is time-consuming and the images aren’t accurate enough.
3. Image Segmentation
Medical image segmentation is the process of breaking down images into different parts such as organs, tissues, blood vessels, or bones. Segmentation reduces the complexity of images and allows doctors to analyze images aptly. AI and deep learning methods of image segmentation are:
- Voxel-based Segmentation: Here algorithm takes a look at each voxel separately and determines the part of the image that has to be segmented.
- Contour-based Segmentation: In this case, the algorithm starts with a contour close to the image/object that has to be separated.
- Convolutional Neural Networks (CNNs): This is a part of the deep neural network and it applies a combination of convolution steps to the image. The algorithm filters out essential data in the picture and determines what it contains.
Benefits of Artificial Intelligence in Imaging
1. Precision Medicine
AI algorithms in healthcare are trained to analyze medical data and produce certain outputs. The role of AI in precision medicine is the same, it helps to improve treatment, diagnosis, and predicts diseases before it occurs, based on the data analyzed.
As per a study, researchers from the University of Texas at San Antonio, the University of Pittsburgh, and UT Health San Antonio have developed a generative AI tool to improve adaptive radiotherapy. The study states that cancer patients who are going through radiotherapy take a CT scan to identify where the tumor is located. At times, a cone-beam computed tomography is also used to determine a tumor. But, the images are difficult to predict and read. So, researchers are leveraging AI to read images clearly and determine tumors, and also to decline the risk of radiation-induced pneumonitis.
2. Spotting Unseen Abnormalities
According to a study published in Nature, a model was developed to differentiate between normal and abnormal CXRs (Chest X-rays) across multiple settings and de-identified datasets. AI algorithm was trained to flag abnormal cases in a general setting. Apart from this, with ample data, algorithms can find abnormalities for any diseases that humans cannot detect. Moreover, algorithms can be fed with enough information to detect stroke, heart attack, fracture, and other adverse events.
It can be difficult to gather patient information to train an AI algorithm for medical imaging due to stringent healthcare rules, but one can work along the lines of maintaining an anonymous patient database. This way, privacy can be maintained, along with patient security. One of the challenges of integrating AI in medical imaging software is to detect problems and convey them to patients before they suffer from the disease.
3. Risk Assessment
Artificial intelligence imaging can be used to identify conditions affecting patients, and it can also determine future potential diseases based on the data fed into the AI algorithm.
As per a study, dissemination of artificial intelligence in breast cancer screening can improve breast cancer risk assessment and help in enabling personalized treatment plans. However, for effective results, it is essential to enhance the interpretability, reproducibility, and robustness of AI and the datasets used for training algorithms.
4. Medical Screening
The use of AI for screening helps to detect chronic diseases such as cancer, cardiovascular diseases, and lung diseases at an early stage. Researchers at Tulane University found that AI can accurately diagnose and detect colorectal cancer from tissue scans better than pathologists. Regularly, pathologists examine thousands of histopathology images to detect cancer, however, they cannot keep up with the workload. So, with the help of AI scans, they can screen tissues for cancer at a faster rate.
Furthermore, AI can be trained to detect left atrial enlargement from X-rays, and to discard other issues related to pulmonary or cardiac. AI tools are also used to automate tasks such as carina angle measurement, aortic valve analysis, and pulmonary artery diameter.
5. Tracking Patients Thoroughly
Machine learning is a powerful technology to monitor and track patients, and note every small change in their health conditions. This approach is especially important while monitoring cancer patients, or any other patient suffering from a chronic disease. AI in imaging helps physicians to track whether a tumor is growing, or shrinking, and how well a treatment is working for the patient. Changes in the tumor, heart conditions, and lung disorders can be picked up at an early stage and treated accordingly.
Standard imaging technology is not capable of brightening the smallest of the smallest pixel, and that’s when AI comes into the picture to clarify images and highlight abnormalities in a body via image segmentation, image registration, or CNN features.
If you wish to know more about AI in healthcare or incorporate AI at your organization, then get in touch with Arkenea, one of the leading healthcare software development companies in the USA. We specialize in generative AI, NLP, predictive modeling, machine learning development, and much more. Our team of expert developers deliver world-class AI-driven software that meet industry standards.