8 Applications of AI in Radiology

Since its inception as a concept in the medical field, Artificial Intelligence (AI) has been viewed with skepticism. Many professionals are concerned that it will be used to replace their expertise, potentially having a negative impact on patient care and outcomes. 

 

In radiology, AI has undergone a transformation. It has evolved as a technology and a market, from a few innovative solutions testing the limits of capability and potential to a virtual deluge of algorithms, platforms, and solutions.

 

In the field of radiology, AI is rapidly advancing. According to an American College of Radiology study, clinical adoption of AI by radiologists increased from 0% to 30% between 2015 and 2020. So, let us learn more about the role of AI in radiology.

 

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AI in Radiology

 

AI is currently focused on improving diagnostic capacity and assisting radiologists in high-profile, acute conditions such as intracranial hemorrhage, pulmonary embolism, and pneumothorax. The ability to assess these conditions in minutes has a significant impact on the patient's outcome. 

 

It is especially useful to be able to triage cases for the radiologist that is more likely to be urgent. The most severe illnesses rise to the top for interpretation, especially in high-volume practices. AI may even aid in addressing one of the most serious issues confronting the field: burnout. 

 

According to one study, 54% of radiologists reported feeling exhausted or depressed. Workloads have steadily increased over the last 20 years as a result of increased imaging utilization, but the work is also becoming more complex.

 

The American College of Radiology recommends increasing radiologist efficiency as one way to reduce stress and burnout, and studies show that AI can help.  AI - powered scheduling software, such as GE Healthcare's Smart Scheduling, uses up to 40 different data factors to understand patient probabilities for missing appointments, potentially reducing no-shows by up to 70%. 

 

Another important function is the ability of AI algorithms to accelerate modality processing. The ability of AI to assist in recognizing the "normal" would be equally beneficial. 

 

In terms of workflow, recognizing normal cases even without a specific diagnosis has a higher value because it allows you to get patients in and out of the emergency department much faster"

 

This is especially important given the rising number of ED visits in the United States. Furthermore, every case in the ED is a STAT case. There are fears that AI will one day replace human radiologists. 

 

 

Applications of AI in Radiology

 

We’ve listed some significant Applications of AI in Radiology below : 

 

1. Identifying Neurological Abnormalities

 

By tracking retinal movements, AI in radiology has the potential to diagnose Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis as examples of neurodegenerative disorders (ALS). Speech analytics is another method for detecting neurological abnormalities because Alzheimer's alters patients' language patterns.

 

Researchers at Stevens Institute of Technology developed an AI tool based on convolutional neural networks and trained it with text from both healthy and affected people. The tool correctly identified early signs of Alzheimer's disease in elderly patients based solely on their speech patterns with a 95% accuracy.

  

This type of software assists doctors in determining which patients with mild cognitive impairment will develop degenerative diseases and how severely their cognitive and motor skills will deteriorate over time. This allows the most vulnerable patients to arrange for care while they still have the ability to do so.

 

2. Classifying Brain Tumor

 

Brain cancer, along with other nervous system cancers, is the tenth leading cause of death in the United States. Traditionally, patients with brain tumors, as well as their surgeons, are kept in the dark prior to surgery. 

 

They both have no idea what type of tumor is present or what treatment the patient will require. A tumor sample is extracted from this mass and analyzed to determine the tumor's classification. Adding AI to the mix in radiology reduces tumor classification time to about three minutes and allows it to be done comfortably in the operating room.

 

Another example is a recent study in the United Kingdom that used machine learning in radiology and imaging to discover a non-invasive method of classifying brain tumors in children. 

 

These tumors are the leading cause of death from cancer in children. Surgeons can prepare a more efficient treatment plan if they know which variant the patient has ahead of time.

 

3. Recognizing Breast Cancer

 

Despite the severity of the disease, doctors miss up to 40% of breast lesions during routine screenings. At the same time, only about 10% of women with suspicious mammograms appear to have cancer. 

 

AI simulation tools in radiology can help to improve the situation. The algorithm was particularly good at detecting early-stage invasive cancers. Some women who develop breast cancer have no symptoms. However, many people were unable to get routine checkups due to the pandemic.

 

According to Dr. Lehman, a radiologist at Massachusetts General Hospital, approximately 20,000 women skipped their screenings during the pandemic. Five out of every 1,000 women who are screened show early signs of breast cancer. 

 

 

4. Radiomics

 

The extraction of features from diagnostic images, with the end result being a quantitative feature/parameter that is measurable and mineable from images. 

 

A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study and correlate them with one another and other data in ways that the human eye or brain cannot. Such characteristics could be used to predict prognosis and treatment response. 

 

By developing patient signatures, AI can aid in the analysis of radionics features. It can also help in the correlation of radionics and other data (proteomics, genomics, liquid biopsy, and so on).

 

 

5. Detecting Fractures

 

The FDA began approving AI algorithms for clinical decision support in 2018. The FDA approved Imagen's OsteoDetect software as one of the first. AI is used in this program to detect distal radius fractures in wrist scans.

 

Imagen's application was approved by the FDA after it submitted a study of its software's performance on 1,000 wrist images. Trust in OsteoDetect grew after 24 healthcare providers who used it confirmed that it helped them detect fractures. 

 

Another application of AI in radiology is the detection of hip fractures. This type of injury is common in the elderly. X-rays have traditionally been used by radiologists to detect this type of injury. However, such fractures are difficult to detect because they can hide beneath soft tissues.

 

A study published in the European Journal of Radiology shows how Deep Convolutional Neural Network (DCNN) can assist radiologists in detecting fractures. DCNN can detect defects in MRI and CT scans that the human eye misses. Human radiologists attempted to identify hip fractures from X-rays while AI read CT and MRI scans of the same hips in an experiment.

 

 

6. Provides the Second Opinion

 

When radiologists disagree on a problematic medical image, AI algorithms can run in the background and provide a second opinion. This practice reduces decision-making related stress and enables radiologists to learn to work alongside AI and appreciate its benefits.

 

Mount Sinai Health System in New York City used AI alongside a human specialist to read radiology results as a "second opinion" option for detecting COVID-19 in CT scans. They claim to be the first institution to use AI and medical imaging to detect novel coronaviruses. 

 

The AI algorithm was trained on 900 scans by the researchers. Even though CT scans are not the primary method of detecting COVID-19, the tool can detect mild signs of the disease that human eyes cannot detect.

 

When a CT scan yields negative results or nonspecific findings that radiologists are unable to classify, this AI model provides a second opinion

 

 

7. Imaging biobanks

 

Thanks to computers' ever-increasing memory capacity, large amounts of data can be stored. The need to store native images and big data derived from quantitative imaging is the primary cause of PACS overload in radiology.

 

Quantitative imaging can produce imaging biomarkers that can be stored and organized in large imaging biobanks (potentially using data from many institutions and locations), available to be processed, analyzed, and used to predict the risk of disease in large population studies and treatment response.  

 

Large biobanks have the potential to become a repository of digital patients (human avatars or digital twins) that AI can use to simulate disease development and progression.

 

 

8. Dose Optimization

 

The ESR EuroSafe Imaging initiative is intended to support and strengthen medical radiation protection throughout Europe through a comprehensive, all-inclusive approach. 

 

EuroSafe Imaging advocates for the use of clinical diagnostic reference levels in CT that are tailored based on appropriateness criteria and patient characteristics (BMI, circulation time, etc.).

 

The choice of protocol, on the other hand, is subject to variation because it is frequently operator-dependent, and as a result, the radiation dose and the quality of the exam vary at both intra- and inter-institutional levels.

 

In this situation, AI can be an optimizing tool for assisting technologists and radiologists in selecting a personalized patient protocol, tracking the patient's dose parameters, and estimating the radiation risks associated with cumulative dose and the patient's susceptibility.

 

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Benefits of using AI in Radiology

 

Physician burnout has become all too common. It is a threat to a professional's well-being and career, and radiology is a high-risk specialty in the medical profession. According to the Medscape Radiologist Lifestyle, Happiness, and Burnout Report 2019, only 25% of radiologists were happy, and 44% experienced physician burnout.

 

Long hours, never-ending tasks, and administration were just a few of the reasons cited as causes of burnout. There's also artificial intelligence. Let’s take a look at how AI benefits Radiology. 

 

• AI has the capability of picking up enough of the image diagnosis weight so that the radiologist can concentrate on the complex cases that require their specialist attention.

 

• AI can pick up enough of the image diagnosis weight so that the radiologist can focus on the complex cases that require their specialist attention.

 

• AI assists the teleradiologist in addition to reducing burnout. Because AI can be used to help diagnose and assess patients over long distances, it helps reduce wait times for emergency patients who must be transported from rural and remote areas.

 

• AI in teleradiology can be used to aid analysis and provide radiologist support. This solution, developed in collaboration with IDG, has so far aided over 400, 000 patients in Western Australia, demonstrating the value of AI in the teleradiology profession.

 

• Finally, another advantage that stands out is how AI in radiology can help support report turnaround times (RTAT).

 

Because the data is embedded within the workflows and can be easily extracted to facilitate report development and delivery, AI solutions have the potential to help speed up RTAT.

 

In terms of the radiologist, intelligent workflow and clinical assistant capabilities can help radiologists to be more: 

 

• Productive: By automating and prioritizing tasks and data feeds.

 

• Quantitative: By providing applications and tools that extract and quantify data semi-automatically or automatically.

 

• Precise: By ensuring that the appropriate information is available, filtered, and presented to support the diagnosis, as well as the repeatability of any quantification processes.

 

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In the last decade, AI technologies and methodologies have begun to empower all aspects of radiology, from imaging data acquisition to imaging data interpretation to clinical decision making. 

 

Despite exciting advances in the field of AI in radiology, there are significant challenges associated with the technological and translational aspects of AI in radiology. regardless of how much technology improves and advances.