Automate clinical processes and detect anomalies human eyes cannot with AI and machine learning
Key Takeaways:
- Artificial intelligence (AI) and machine learning (ML) can be used by radiologists, pathologists, and oncologists for diagnosis, segmentation, classification, and other medical applications
- Minute changes that are often missed by the human eye can be detected with ML algorithms
- The technology has applications for use in diagnosis and treatment of cancers, cardiology, thoracic conditions, and musculoskeletal problems, among many others
- AI mimics human intelligence, and ML uses algorithms that learn over time and with experience to ferret out important image information
- AI already has proven that it may be a valuable ally for radiologists and pathologists who want to accelerate their productivity and improve their accuracy
Artificial intelligence (AI) and machine learning (ML) work in everything from chatbots to self-driving cars, but these technologies have only recently been adopted for medical imaging. Radiologists, pathologists, and oncologists are embracing them for diagnosis, segmentation, classification, and other medical analysis applications.
AI is actually the overarching name for a branch of computer science concentrating on building machines that are capable of performing tasks that usually require human intelligence. ML is a type of AI that uses data and algorithms that learn through experience, imitating how humans learn. AI applies that machine learning to solve problems.
When it comes to medical images, AI can automate steps of the clinical process as well as provide support for decisions. It’s disrupting healthcare in general and offers numerous benefits, especially for radiology. Let’s take a closer look at the role AI and ML are beginning to play in medical imaging.
AI and ML aid understanding of disease processes
It’s early days in the use of AI and ML for medical imaging, but its potential has not gone unnoticed. Unique opportunities exist to learn about fine imaging changes that reflect processes of disease that are poorly understood.
One example of effective use of Ai and ML in medical imaging is with an emerging, potentially fatal complication of immunotherapy: myocarditis. Cardiac imaging can be done early and facilitate timely treatment. How AI and ML come into this is in the identification of specific patterns in MRIs that narrow down the diagnosis. AI and ML also could identify subtle cardiac abnormalities that have a clinical correlation when applied to echocardiography.
Cancer detection and characterization presents another use for AI and ML in medical imaging. Quantitative analysis of fine structural image changes could be used to predict malignancy odds and tumor kinetics, which in turn can help personalize management plans.
The rise of AI and ML is a positive development in medical imaging and can work just as well, if not better, than humans to identify image features precisely and with speed. Let’s look at some real-world use cases.
How AI and ML can improve disease detection and diagnosis in medical imaging
The American College of Radiology Data Science Institute has come up with a list of use cases that will help radiologists and connected professionals ensure that AI tools give them what they need informationally, can be inserted into workflows, and can improve patient care both qualitatively and efficiently. These include abdominal, cardiac, musculoskeletal, oncological, pediatric, thoracic, and non-interpretive imaging. Let’s take a closer look at some of the top use cases of AI and ML for medical imaging.
- Spotting cardiovascular abnormalities
Cardiovascular disease risk can be revealed by measuring the various structures of the heart. It can also identify problems that may need surgery or medication management. Through AI, automating the detection of abnormalities in tests such as chest X-rays can lead to faster decision-making as well as decrease diagnostic errors.
A good example of this is when a patient comes to the emergency department complaining of shortness of breath. A chest x-ray is usually the first available image. With AI, this can be used as a fast screening tool for cardiomegaly, which is a marker for heart disease. In this case, a quick visual assessment by a radiologist can be inaccurate.
In addition, using AI to identify left atrial enlargement from chest x-rays could rule out other cardiac or pulmonary issues to help providers target the right treatment. AI tools could also be used to automate additional measurement tasks. Applying AI to imaging can identify thickening of muscle structures and monitor changes in blood flow, for example.
ML algorithms could automatically populate reports that identify abnormal measurements or values to save time for clinicians.
- Finding musculoskeletal injuries and fractures
If fractures and musculoskeletal injuries are not treated quickly and properly, they can result in long-term chronic pain for patients. Using AI to identify hard-to-see fractures, dislocations, or soft tissue injuries means surgeons and specialists can make better, more confident choices in treatment.
When accompanied by trauma, fractures can sometimes be overlooked by diagnosticians. Fracture types are often hard to find with standard images, however, AI tools may be more likely to see the subtle variations that could indicate a problem that requires surgery.
Through ML, unbiased algorithms can be created to review images, which, in the case of trauma patients, could help make sure that all injuries are detected and receive proper care leading to positive outcomes.
- Diagnosing neurological diseases
Degenerative neurological diseases are a devastating diagnosis. AI could be especially effective in distinguishing amyotrophic lateral sclerosis (ALS) from primary lateral sclerosis (PLS). Both rely on imaging studies for diagnosis, and it’s up to radiologists to decide if found lesions are relevant or mimic the structures of these diseases. False positives are quite common.
The necessary manual segmentation and quantitative susceptibility mapping assessments are time-consuming and difficult. Using ML to automate this procedure would spur research as well as assist in developing new biomarkers, an important area of research. Algorithms could also flag images with suspect results and provide risk ratios for evidence of ALS or PLS.
- Screening for cancer
AI could be especially useful for detection of head and neck cancers, prostate cancer, and cervical cancer, among others. When it comes to breast cancer, AI could more accurately categorize microcalcifications using quantitative imaging features. These microcalcifications are difficult to conclusively classify as malignant or benign.
Polyps, a precursor to colorectal cancer, can be missed by less experienced radiologists when looking at CT colonography. With AI, accuracy and efficiency of polyp detection is improved, and false positives are reduced. For those with established cancers, AI could detect metastasis through an ML algorithm.
- Discovering thoracic conditions and complications
Pneumonia and pneumothorax require speedy reactions from providers. Pneumonia can be life-threatening if left untreated. Radiology images are often used for diagnosis and to distinguish these from other lung conditions. However, radiologists may not always be around to read the images, and if they are, it’s difficult to identify in a patient with pre-existing lung issues, such as malignancies or cystic fibrosis. Subtle pneumonia may go undetected altogether.
An ML algorithm could assess images for the opacities that indicate pneumonia and alert providers, who can then provide quick treatment. ML can also be used to identify suspected pneumothorax and prioritize the type and severity. Changes in the size of detected pneumothoraxes could also be tracked.
AI and ML show great promise in the field of medical imaging to improve and automate the practice of medicine, offer more accurate diagnoses, more targeted treatment, and better patient outcomes.
Intravision XR – greater visibility for improved patient outcomes
Provide your AI and ML tools with the best images possible. For intraoperative surgical guidance, pre-surgery planning, and post-surgical analysis, you need fully-formed 3D models. Intravision XR, a cloud-based 3D modeling software, creates these models from DICOM datasets such as CT and MRI scans. They contain complete anatomical detail and can be viewed using AR, VR, or the standard screen on a phone, tablet, or computer.
For more information about how we can equip you for not only today’s technology but the future of medicine, please contact us today.
