Artificial Intelligence (AI) is embraced massively and has become popular in hybrid imaging, including ultrasound, radiology, and nuclear medicine. Artificial intelligence can assist physicians in making precise and reproductive imaging diagnosis and reduce the physicians’ workload. The incorporation of hybrid technology- positron emission tomography/computed tomography (PET / CT) and single-photon emission computed tomography (SPECT)/CT - has transformed the hybrid imaging field.
The technology provides a combination of the exquisite anatomical details provided, by CT, with the necessary and much-required functional, physiological or metabolic data provided by molecular imaging. Hybrid imaging technology can provide "one-stop" imaging with improved precision, attenuation, and localization, thereby offering a more accurate diagnosis. The use of PET/CT is well-recognized in clinical practice and has added substantial value in the fields of cardiology, neurology, and oncology.
Hybrid imaging is the fusion of more than two imaging technologies that form a new technique. The combinations of the innate benefits of the fused imaging technologies form a novel and much stronger modality. Some of the alternative hybrid imaging techniques include Photon Emission Transmission (PET)/Computed Tomography (CT), Ultrasound and CT, Ultrasound and Magnetic Resonance Imaging (MRI), MRI and CT, and others. Hybrid imaging technology can enhance the precision, attenuation, correction, and localization of one-stop imaging, thereby offering a more accurate diagnosis.
In oncology, various hybrid imaging methods play a key role in the diagnosis, staging, re-staging, monitoring, and follow-up of different types of cancer. Stand-alone morphological imaging such as computerized tomography (CT) and magnetic resonance imaging (MRI) offer a high degree of anatomical details about the tumor.
Stand-alone functional imaging such as positron emission tomography (PET) and single-photon emission tomography (SPECT) is plentiful in functional knowledge but offers limited insight into tumor morphology. The launch of the first hybrid imaging modality PET/CT is one of the most successful milestones of the current era that has revolutionized oncology patient care with high diagnostic accuracy.
Automated quantification technologies have entered a level of maturity and acceptance in the market. Along with this, AI has been able to make measurements from hybrid imaging and auto-filling fields or perform calculations that were otherwise manual and time-consuming. The technology is based on several items already in clinical use, including most of the premium echocardiography system.
Newer AI applications of computer-aided diagnosis and clinical decision-making support have only recently been introduced in the market and may take many years to be established for general use. The primary fields where AI Image Diagnostic Technology is being developed and commercialized are important findings such as stroke where timing is essential. Certain areas include identifying incidental findings and resources to reduce the amount of time required to analyze complex examinations such as cardiac magnetic resonance imaging (MRI). AI is also being developed to assist patients on auto triage who require additional or more urgent care.
The retina is part of the nervous system has the significant advantage of being visualized by transparent ocular media, making it one of the favorite regions to study neurodegenerative and regenerative processes. Recent advancements in the field of ophthalmic hybrid imaging such as optical coherence tomography (OCT) now allow non-invasive, three-dimensional retinal events to be investigated over long periods.
Recent studies have shown that AI has an amazing potential to perform much better than human beings in certain tasks, particularly in the field of image recognition. As the amount of image data in the ophthalmology imaging center is increasing significantly, there is an urgent need to evaluate and process these images. AI has been applied to the deciphering of medical data and has made extraordinary progress in intelligent diagnosis.
This cancer detection technology enables a three-dimensional (3D) restoration of the breast tissue, which can then be viewed as sequential slices of the breast. This new technology for hybrid imaging can drastically reduce errors and allow for a thorough examination of even dense tissue. Tomosynthesis facilitates the detection of minute lung nodules and chest pathologies that can be undetected by conventional methods. Using 3D visualization helps to visualize cancer anatomy in patients and to assess the stage of the disease more precisely.
The medical field is actively taking advantage of AI to address issues such as increasing work efficiency, improving the quality of treatment and improving medical care outcomes. Diagnostic hybrid imaging is one of the fields of medicine considered to be at the earliest opportunity for the practical application of AI. Although much is still in the research phase, tools that have been FDA-approved for diagnostic help are starting to appear in the USA and are progressing towards their practical application. Under its hybrid learning framework, Hitachi is driving research and the development of innovative diagnostic imaging AI technology that leads to high-precision, medically relevant outcomes.
The increasing demand for more advanced hybrid imaging procedures can be associated with an increase in the number of patients with chronic diseases, such as cancer, Alzheimer's disease, and numerous cardiovascular diseases. This has led to an increase in demand for better contrast media and agents. Governments all over the world are issuing more approvals for the use of specific contrast agents in imaging procedures.
Advancements in MRI methods, such as superconducting magnets, open architecture, and software applications, have sparked interest in academic institutions, research laboratories, hospitals, and physicians around the globe. Furthermore, with the advent of MRI-compatible pacemakers, more patients became eligible for MRI procedures.
Feb-2019: Canon Medical launched the Alphenix platform, the next-generation technology of interventional systems. This hybrid imaging platform integrates all-new features that enable clinicians to deliver accurate and clear images without compromising the workflow as well as prioritizing the low dose.
Mar-2019: Canon Medical made advancements to its PET/CT system at Celesteion. The enhancement involved a wide range of new acquisition and reconstruction techniques to improve its CelesteionTM PET/CT system's workflow and image quality.
Jul-2019: Philips launched Azurion with FlexArm to set a new standard for flexible positioning and image-guided procedures for patient imaging across India. This product launch extended its operations to the Indian market.
Dec-2019: Canon Medical launched a spectral CT method, Aquilion ONE/PRISM Edition. This system is designed for deep intelligence, combining the technologies of artificial intelligence (AI) to optimize spectral and traditional CT capabilities and automated workflows alongside insightful clinical insights. These insights assist the doctors in making more informed decisions over the care cycle of the patient.
The hybrid imaging market is set to skyrocket at an exponential pace due to a wide range of factors. SPECT/CT is evolving rapidly as an important and useful diagnostic modality that provides distinct advantages as compared to planar and SPECT imaging separately. The benefits of the hybrid imaging technology include better location, the ability to distinguish physiological from pathological processes and to identify unsuspected diseases, and the potential for change in management in some patients.
Free Valuable Insights: Global Hybrid Imaging Market to reach a market size of $9.1 billion...
It also guides the surgical approach and reduces the duration of surgery and anesthesia, resulting in both reduced morbidity and cost. The intensity of the CT attenuation provided by SPECT/CT, mainly in cardiac studies, enhances specificity and accuracy. This new modality provides improved diagnostic precision by integrating anatomy and function in one sitting, thereby providing an advanced and precise diagnosis.
The medical and professional entities of the radiology field strongly believe that Artificial Intelligence (AI) will emerge as the most impactful development for their sector over the coming years. Artificial Intelligence, specifically with the deep learning algorithms, has gained extensive momentum for its flawless performance in image-recognition operations. The technology can establish a higher accuracy for diagnosis with optimized efficiency.