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How is AI Revolutionizing Medical Imaging?

  • Vrinda Mathur
  • Sep 10, 2022
How is AI Revolutionizing Medical Imaging? title banner

Artificial intelligence (AI) has been around for decades and is still evolving as technology advances. In health care, AI can be used to streamline the check-in process for patients, improve the efficiency of patient records, monitor disease, aid in the diagnosis, assist in surgical procedures, and provide mental health therapy. AI in radiology aids in a variety of processes, including patient scheduling, billing, optimizing staffing, developing protocols, assessing image quality, reducing radiation dose, and image interpretation. AI should not be feared because it will not replace humans; rather, it should be welcomed for its ability to improve and extend lives.

 

AI is present in every industry, from e-commerce to healthcare. Medical imaging is the area of the healthcare industry where AI has seen the most use. Medical imaging results contain a wealth of patient data and necessitate high precision. Further care and treatment for a patient are decided based on medical imaging results. AI in imaging can help radiologists read medical imaging reports more accurately by reducing human errors.

 

 

What is Medical Imaging

 

Medical imaging is a cutting-edge technology that enables effective patient care by screening, diagnosing, and treating patients. Medical imaging technology enables healthcare providers to make medical decisions based on the best available information by allowing them to see accurate, detailed images of a patient's body. As medical imaging science and technology advance, innovations will help improve patient outcomes, ensuring that Americans live longer, healthier lives.

 

All diagnostic and therapeutic investigations/interventions performed in a typical radiology department are housed in medical imaging. It includes various imaging modalities and processes for imaging the human body for diagnostic, treatment, and follow-up purposes. It plays an important role in public health initiatives for all population groups. It includes the following:

 

  • X-rays (including eg plain x-rays, DEXA scans, fluoroscopy)

  • Magnetic resonance imaging (MRI) (MRI)

  • Ultrasound (US)

  • CT (computed tomography) (CT)

  • Nuclear medicine: cross-sectional radiotracer scanning, for example. PET is regarded as a distinct modality from 'traditional' scintigraphy, such as bone scans.

  • Modes of hybridization

 

Medical imaging, particularly X-ray examinations and ultrasonography, is critical in a wide range of medical settings and at all levels of health care. Effective decisions in public health and preventive medicine, as well as in curative and palliative care, are dependent on accurate diagnoses. Though medical/clinical judgment may suffice before treatment of many conditions, the use of diagnostic imaging services is critical in confirming, correctly assessing, and documenting the courses of many diseases, as well as assessing treatment responses.

 

Non-invasive medical imaging allows doctors to better assess patients' bones, organs, tissue, and blood vessels. The procedures aid in determining whether surgery is an effective treatment option; locating tumors for treatment and removal; locating blood clots or other blockages; directing doctors dealing with joint replacements or fracture treatment; and assisting in other procedures involving the placement of devices – such as stents or catheters – inside the body.

 

Overall, medical imaging has improved diagnoses and treatments by significantly reducing the amount of guesswork performed by doctors, allowing them to deal with patients' injuries and diseases more effectively.

 

In hospitals, medical imaging has been critical in diagnosing and monitoring various types of critical diseases. The generation of images of various body parts and tissues is known as medical imaging. For example, if a person is diagnosed with lung problems, X-rays monitor the lungs and determine which part of the lungs is affected. CT scans are used to get a better view of the part of the lungs if the image is not clear enough to draw any conclusions. Every year, hospitals generate massive amounts of image data. These images can be used to train deep convolution neural networks, which can aid in the detection of various patterns in the images. Artificial intelligence will aid in the analysis of these images, and it will also further help in diagnosis and provide tools for Doctors to make life-saving decisions.

 

 

Fields in Medical Imaging Using AI :

 

AI-based Medical Imaging is dependent on images produced by various hospitals. These images are used to train various algorithms to detect various patterns, which are then analyzed to detect various anatomical anomalies. These analyses and pattern recognition will help radiologists and doctors diagnose and treat diseases more quickly. The predictive capability of AI-powered technology can aid in detecting malignant tissues or cancerous cells before they become fatal.

 

The collective output of machines has greatly improved since the advent of AI. Some of the most primitive and time-consuming machine-based functions are greatly simplified by the use of complex AI programming algorithms. AI is now affecting almost every industry sector. In recent years, the Healthcare sector has seen a gradual increase in the adoption of AI-based technology for various healthcare divisions. AI in healthcare is widely used in areas such as patient care, drug discovery, and personalized treatment. With significant technological AI advancements, medical imaging has emerged as one of the most promising healthcare segments. This improvement has been shown to improve the quality of internal imaging using various radiological imaging techniques such as X-ray radiography.

 

Some of the prominent fields of medical imaging that are using AI Technology are discussed below:- 


Fields using Ai in Medical Imaging 1. Breast Mammography 2. Cardiovascular MRI 3. Imaging of Lungs 4. X- rays 5. Other Areas

Fields using AI in Medical Imaging 


 

  1. Breast Mammography:

 

Artificial intelligence assists radiologists in the diagnosis of breast abnormalities and the detection of cancerous cells or tissues. QuantX (Quantitative Insights) CADx software uses Deep Learning-based tools to assess and characterize breast abnormalities using magnetic resonance imaging (MRI). Imaging techniques such as Digital Breast Tomosynthesis (DBT) or 3D Mammography have aided radiologists in making life-saving diagnoses in projects such as ProFound AI (iCAD inc.).

 

 

  1. Cardiovascular MRI:

 

AI aids in the detection and identification of potential strokes in patients. ContaCT (Viz.ai), an AI-based technology, analyses computed tomography (CT) images for potential stroke. Deep learning is used to detect large vessel occlusions (LVOs) in scans. Artery's Cardiac MRI uses Machine Learning and trained models to help automate complex cardiac analysis.

 

 

  1. Imaging of the Lungs:

 

Lung imaging is another field that has benefited from AI. AI-based systems assess lungs and use medical images such as CT scans to detect potential lung cancer. Infervision, a Chinese AI-based start-up, developed a technology called Augmented CT screening Solution (AI — CT) that can detect suspicious lung cancer lesions in CT scans.


 

  1. X-rays:

 

X-ray machines are commonly used to generate black and white images of the human body to detect dental decay, tumors, and arthritis. While CT scanning is frequently chastised for its ionizing radiation, X-rays, which have a lower level of electromagnetic wave exposure, have their drawbacks, including limited accuracy in chest tests, which results in missed lesions, including early-stage lung cancer. In these cases, AI can not only accelerate testing but also improve image interpretation. Google, for example, developed deep learning models to help classify clinically important findings on frontal chest radiographs to improve the capabilities of X-rays.

 

 

  1. Other Areas:

 

Other fields, such as Brain Imaging or Neuroimaging, have successfully used AI to detect potential brain injuries or tumors. OsteoDetect is software that uses machine learning techniques to identify and highlight distal radius fractures in wrist radiographs (X-rays). Idx-DR is a computer-aided diagnosis (CADx) software program that analyzes images of the eye for signs of diabetic retinopathy, the most common cause of vision loss in diabetic Americans.

 

Also Read | 10 Uses of Machine Learning in Healthcare 

 

 

Benefits of AI in Medical Imaging

 

In the healthcare industry, the most common problems that Artificial Intelligence solves are those involving speed, accuracy, and workload. AI capabilities improve the internet of things in medicine, including hospital automation systems, telehealth solutions, and medication trackers, in addition to IoT-based tools that generate images of human bodies. Here are some of the potential benefits of AI techniques in medical imaging (for both diagnosticians and patients) - 

 

  1. Automation: AI improves workflow automation by automatically registering images, segmenting anatomies, and measuring lesions. It also aids in the interpretation of legal cases. In some cases, technicians with a radiology degree are becoming an option rather than a requirement.

 

  1. Greater precision: Artificial intelligence improves diagnostic accuracy by feeding algorithms with larger image datasets, which aid in the detection of cancerous cells or lesions in eye tissue.

 

  1. Cost: Adoption of AI lowers the cost of medical imaging tools and diagnostic procedures, giving more patients around the world the opportunity to be tested.

 

  1. Productivity: Doctor productivity is increased by AI-assisted software that can analyze and interpret images faster than humans; AI-based solutions accelerate image processing in healthcare.

 

  1. Image clarity: Denoising and reconstructing images result in an overall improvement in image quality.

 

  1. Workload: Reduced workload reduces waiting lists and increases the number of tests that can be performed.

 

  1. Speed: When patients are in CT or MRI machines, AI methods reduce screening time.

 

  1. Level of Radiation Exposure: AI techniques can accurately reconstruct high-quality images from low-quality originals, reducing the radiation exposure for patients during CT screening and X-ray exams.

 

 

BottomLine: Future of AI in Medical Imaging: 

 

Medical images are a valuable source of patient data, but they are becoming increasingly difficult to interpret precisely. The adoption of software-based value-added services powered by IoT and data analytics is likely to automate and simplify this process. It can, for example, support computer-aided diagnosis, large-scale screening programs, and data integration platforms that provide a unified view of a patient's health. Soon, every medical imaging machine will be linked to the cloud, where cutting-edge algorithms will analyze data and assist doctors in screening, assessing, and diagnosing patients.

 

The algorithm's evaluations are more reproducible and less subjective. According to studies, AI algorithms are more effective and efficient than human experts at identifying diseases. AI algorithms can read images faster and more accurately (up to 95% in some cases). Emerging research, for example, suggests that AI could play a significant role in improving breast cancer screening outcomes.

 

An artificial intelligence model reduced false positive results by 25% and radiologist workload by more than 62%. AI solutions can also provide real-time support to clinicians by analyzing massive amounts of historical patient data. For instance, hospital readmission risks indicate a patient's increased likelihood of returning to the hospital within 30 days of discharge.

 

Before healthcare organizations adopt AI for autonomous diagnosis, there is still a long way to go. However, the future appears bright. To address the trust and reliability issues, the success of AI in medical imaging will be dependent on education and training. To obtain the most precise diagnosis, radiologists, pathologists, and physicians must understand how to integrate AI into clinical practice and evaluate its efficacy, as well as when to apply the appropriate human intervention.

 

In conclusion, AI-based Medical Imaging and Technology will only grow and assist future radiologists and doctors in making critical medical decisions. Its efficiency and dependability ensure that thousands of lives will be saved. More research and work are needed to advance AI in the health sector, which can be detrimental to humanity.

 


Next Read | How AI improves Accessibility for People with Disabilities

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