The application of artificial intelligence (AI) in medical imaging has marked a new era in healthcare, profoundly impacting modern medicine. Innovations within AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, have significantly enhanced the accuracy and efficiency of medical image analysis.
By integrating advanced AI techniques with their practical applications, we can expect AI to continue shaping the future of healthcare in highly positive directions.
Medical imaging is a crucial method that helps doctors and patients understand the morphology, function, and physiological structure of the body, thereby enabling effective prevention and treatment strategies. Medical imaging involves the use of techniques such as X-ray, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI).
In mid-1895, Wilhelm Conrad Rontgen, a physics professor at the University of Wurzburg in Germany, discovered X-rays. He performed the first X-ray examination in the world, giving birth to modern medical imaging. Rontgen was later awarded the first Nobel Prize in Physics.
Since then, the field of medical imaging has undergone various stages of development and continues to progress, becoming a key area that receives significant investment and attention.
AI in medical imaging involves using advanced algorithms and machine learning techniques to analyze and interpret medical images. Artificial intelligence (AI) not only enhances the accuracy and efficiency of processing complex images but also contributes significantly to improving diagnosis and personalized treatment planning for patients, leading to better treatment outcomes.
In recent years, the rapid advancement of big data has driven significant progress in the development and optimization of AI models. Research on AI in medical imaging has become increasingly prevalent and widely applied. With its current capabilities, AI in medical imaging plays a crucial role, supporting doctors throughout the entire process of medical imaging, from quickly generating high-quality images at low cost to analyzing, diagnosing diseases, and automatically generating reports.
Using AI for medical imaging not only automates the process but also fundamentally changes the approach to disease diagnosis, improving the accuracy and efficiency of the entire process.
AI-assisted medical imaging enables healthcare professionals to detect diseases more quickly, allowing for earlier intervention. Typically, radiologists spend considerable time reading and analyzing images meticulously. AI-powered medical imaging tools help doctors make clinical or subclinical decisions more swiftly and accurately.
When integrated into medical imaging, AI technology can improve the accuracy of diagnosis and treatment. For instance, AI and machine learning tools can distinguish between different types of lung cancer, enabling more precise diagnosis and appropriate treatment planning. Additionally, AI-assisted medical imaging can more accurately predict cancer patients' survival rates based on tumor development stages.
With these precise measurements, doctors can now more easily provide personalized treatment plans that align with patients' conditions.
AI-assisted image analysis provides decision support and shortens diagnostic times, helping to alleviate physicians' workload. In traditional cancer screening methods, doctors must label and evaluate thousands of images to detect cancer cells, significantly increasing their workload and extending diagnostic times.
AI medical imaging tools can automate the analysis process, reducing manual workload. Moreover, this technology enhances healthcare providers' capabilities by decreasing analysis time and addressing the global shortage of medical specialists.
Whether through timelier diagnosis or more accurate intervention, AI technology in medical imaging has proven its ability to enhance overall treatment outcomes for patients.
For example, in emergency care scenarios like stroke, AI tools can save time by providing rapid and accurate diagnoses. In some cases, AI-assisted imaging for stroke can reduce the time from CT scan to intervention from 281 to 243 minutes.
Most current AI diagnostic applications for X-rays provide estimates of the likelihood that a patient may experience complications based on X-ray images. For instance, an AI system might conclude that a breast lesion has a 10% chance of being malignant. A radiologist might choose to perform a biopsy, but the AI system might not fully grasp the severity of the issue and could consider a 10% cancer risk as insignificant for a biopsy.
Designing accurate AI algorithms is as challenging as integrating AI systems into daily radiology workflows. The roles and responsibilities of radiologists may change, and workflows may need to be adjusted accordingly. However, it cannot be entirely expected that algorithms will make completely autonomous clinical decisions, regardless of their accuracy and efficiency.
Despite the numerous applications of AI in medical imaging, many healthcare providers are still not ready for digital health transformation. Their research and patient data are fragmented, security measures are outdated, hardware needs upgrading, and systems lack interoperability. Integrating AI into such an environment may create additional barriers.
The use of artificial intelligence (AI) in clinical data science and radiology is rapidly increasing. Let’s explore the top 5 standout AI applications in medical imaging with VinBrain.
One popular AI application in medical imaging is detecting abnormalities in chest X-rays. With the aid of AI, doctors can accurately diagnose abnormalities, even those as small as minute or faint nodules.
bất thường trên hình ảnh của X-quang ngực thẳng. Với sự giúp sức của AI, bác sĩ có thể chẩn đoán vô cùng chính xác những bất thường dù chỉ ở kích cỡ rất nhỏ hoặc những nốt mờ.
VinBrain’s DrAid™ CXR Screening, an FDA-approved AI solution for healthcare in Southeast Asia, can detect and classify up to 54 abnormalities related to lung, heart, mediastinum, and bone diseases. This product’s early and accurate detection capabilities have been reported to enhance doctors’ efficiency by up to 30%, with diagnostic accuracy improving by up to 25%.
In addition, VinBrain also has several other AI solutions for medical imaging. These solutions are being used in over 100 healthcare facilities both in Vietnam and internationally.
A notable application of AI in cardiac imaging is predicting patients’ risk of developing arrhythmias by analyzing electrocardiograms. AI can improve cardiac image display by colorizing heart chambers on black-and-white echocardiograms in real time, streamlining radiologists' workflow.
Philips has developed an AI-based system called HeartModel, which creates colored heart models and projects dynamic 3D images showing cardiac motion and changes in left ventricle (LV) and left atrium (LA) volumes during the cardiac cycle.
To accurately determine tumor types, doctors need to perform biopsies, MRI scans, and blood tests. Once the tumor type is identified, AI tools in radiomics (data imaging) can classify tumors into different grades.
AI has accurately classified brain tumor grades with very few false positives or false negatives. A surgical diagnostic study shows that AI can classify brain tumors in under 150 seconds compared to 20-30 minutes using traditional methods. This means doctors can rely on AI as a tool to support clinical decision-making for the best treatment approaches.
Reporting is a time-consuming and error-prone task, often causing frustration for radiologists. Moreover, the lack of standardized reporting leads to variability and incompatibility in data provided by doctors.
Natural language processing tools offer valuable capabilities to optimize medical imaging reporting, from swiftly converting speech to text to automatically compiling and structuring reports to improve comprehension. Besides imaging reporting, AI-based solutions can also perform related tasks, such as enhancing image scanning quality.
German AI startup Vara collaborated with the Northern Mammography Reference Center in Oldenburg to conduct a study evaluating 2,396 mammograms from women later diagnosed with interval cancer. AI detected and accurately identified 27.5% of false-negative cases and 12.2% of minimal sign cancers. This means AI can identify breast cancer signs that many radiologists might miss in the earliest stages of the disease.
Sources:
https://www.onixnet.com/blog/how-ai-powered-medical-imaging-is-transforming-healthcare/
https://vinbigdata.com/thong-tin-bao-chi/ung-dung-ai-trong-chan-doan-hinh-anh-y-khoa-tai-viet-nam.html
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