AI in Clinical Image Analysis | Medical Imaging

AI in Clinical Image Analysis

Artificial intelligence is one of the most potential technologies emerging as a catalyst of the digital era. In every single business industry, artificial intelligence is making wonders and heading the industries towards more precision and perfection. The medical industry is not an untouched factor anymore from automation and artificial intelligence. Over recent years, the medical industry is witnessing giant transformations and artificial intelligence is becoming one of the major reasons behind these revolutionary changes. The automated analytical systems are capable of scanning medical images and it is all due to the potential of artificial intelligence. It is helping the medical industry to become more accurate with a precise diagnosis of various diseases.

Deep learning architectures powered by artificial intelligence are emerging as the latest technology for clinical image analysis. With this technology, now analyzing lungs, retina, digital pathology, nerves, heart, skeleton, abdomen, and more have become very simple and precise.

Need of AI in clinical image analysis:

If you check the medical statistics then most of the mortality is due to the delay in the detection of several life-threatening diseases. The medical industry was in bad need of a non-invasive technology that can help professionals to detect the disease at an early stage and start the best treatment as soon as possible. Well, there were several quantitative assessment techniques for evaluating clinical images but most of those were found inaccurate and demanded serious consideration for it. Artificial intelligence has given a perfect solution for all the above-mentioned problems.

There are several applications of artificial intelligence in the field of clinical image analysis, a few of those are listed below:

Brain imaging:

Using AI clinical image recognition a plethora of segments related to the brain can be diagnosed, such as tumor detection, anatomical analysis of the brain, AD cognitive classification, and more. The AD classification with artificial intelligence is considered as the superior algorithm. In traditional days, the analysis was done manually which was time-consuming, and also the results were not precise. A study was conducted to determine the efficiency of both types of brain imaging and the analysis conducted with AI technology was proven way more effective when compared with the traditional ones.

Screening for cancer:

Medical imaging is done to determine several types of cancer, such as colon cancer and breast cancer. In breast cancer, it is difficult to identify whether the microcalcification in tissue is benign or malignant, and a false positive report can lead to redundant invasive treatments. Also, there is a chance of missing malignant and getting delayed in detecting cancer and starting the treatment. Here comes the role of artificial intelligence that helps the professionals to improve the screening methods and get better and more precise results in the shortest possible time. It also provides risk scores to the specialist helping them to make more informed decisions. Artificial intelligence screening gives improved accuracy by automatically analyzing quantitative imaging features.

CTC scan is done to determine the rectum and colon area to detect clinically significant polyps. It was a time-consuming procedure and less experienced professionals may take a longer time to complete the examination. AI can help all radiologists regardless of their experience to attain more accurate results in minimal possible time.

Flagging thoracic complications:

Certain health conditions require immediate detection and treatment from the healthcare professionals, such as pneumonia and pneumothorax. Such health conditions are the major targets of artificial intelligence technology as they may be life-threatening if these diseases are left untreated. Using traditional way of examination always required radiologist to read the results and the radiologist may not be always available to read the results, this may ass a delay in the treatment. Even if the radiologist is available then also it can be difficult for a radiologist to determine pneumonia if the patient is already suffering from any lung disorder or any medical condition.

In case a patient is having subtle pneumonia below the digraph’s dome on the front chest, then the healthcare provider can overlook the condition and suggest an unnecessary CT scan.

AI can help the practitioner to determine the patient’s condition more accurately by assessing the X-rays and other images and giving a prompt alert to the provider about the diagnosis.

Similarly, AI also helps healthcare providers to identify pneumothorax in the patients at an early stage. In pneumothorax, air pockets get generated in between the wall of the lungs and chest. It is more likely to get undetected using traditional examination methods. This may lead to a threat to human life. AI helps the radiologist to determine the lungs and chest condition of the patient in detail and get all segments intervened with prompt diagnosis.

Conclusion:

Artificial intelligence is changing the entire scenario in the medical industry by bringing more accuracy to health interventions by automating clinical image analysis. Looking at the rate of diseases and their severity, the healthcare sector has started partnering with the IT industry to incorporate the power of AI into their medical interventions and make the clinical image analysis more accurate than ever. With the growing technology, it is predicted that AI will become as smarter as a human brain and will be able to take a more accurate decision without any human interventions.



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