How an AI platform could help create better Screening Campaigns
The majority of global disease burden and mortality are caused by non-communicable diseases. However, a sizable portion of patients in both high-income nations and low-income and middle-income countries are still uninformed that they have these life threatening illnesses.
Previous research has revealed that a significant fraction of people with hypertension, lung disease, and other chronic conditions go misdiagnosed, and that about half of adult diabetes cases go undiagnosed globally.
Early detection and classification are essential for a timely management, stopping the course of the disease, and lowering its morbidity and death. However, it might be difficult to screen people who do not have access to a doctor or who reside in rural locations far from medical services.
Lack of access to diagnostic techniques that are sensitive enough to screen groups that are disadvantaged and underfunded is a big obstacle. Many diagnostic systems are extremely costly, invasive, or need skilled healthcare professionals to operate, making their use in environments with limited resources impossible. Due to the rising demand for screening tests, the lack of qualified doctors, and the increased awareness of preventative healthcare, providers must deal with rising expenses of illness diagnosis. In actuality, more than 97 percent of people who were examined had no apparent issues but still needed roughly the same degree of medical attention from a qualified professional.
Doctors need to collaborate with technology
The responsibility of replicating the standard screening procedure at scale falls on healthcare providers, which has an impact on their operational effectiveness and restricts patients’ access to inexpensive treatment.
A potential answer to this problem is provided by AI technology. AI-assisted medical image analysis and pathology identification help clinicians to diagnose more quickly and accurately, allowing them to treat more patients, boost recovery rates, lower screening costs, and provide revenue opportunities for healthcare providers.
Such technologies offer low-cost screening of various medical diseases, even in remote locations, by gathering data from several sensors and using machine learning algorithms to analyze the models. Access to technology has increased significantly over the last ten years, especially for modern devices like phones and PCs. This technology, when paired with AI, has the potential to make diagnostic procedures achievable in every corner of the planet.
Front-line healthcare professionals with AI capabilities will use clever algorithms to non-invasively test for a range of illnesses. These technologies will detect them sooner and enhance health outcomes.
What are the lung tests used for screening
One of the most used imaging techniques in the medical industry is the chest X-ray. CXR offers a good assessment of the patient’s thorax with 0.1 mSv radiation exposure, which is comparable to 10 days of background radiation from natural sources. The computer-aided diagnostic (CAD) system for chest XRays has been developed since the 1960s, long before digital imaging. Before being forwarded for additional analysis, image attributes including form, size, intensity, and texture needed to be manually identified.
Computers are now capable of performing direct picture analysis. Radiomics (or the extraction of mineable data from medical imaging) broadens the definition of image attributes from a computer standpoint by calculating the picture pixel-by-pixel. The region of interest may be translated to higher dimension data and expressed as a large matrix by calculating the picture texture and density using various mathematical approaches.
To obtain accurate radiomics data, image augmentation is an important procedure before nodule detection, including pre-processing, lung segmentation, and rib suppression.
CT technology provides a noninvasive method to explore the 3-dimensional structure of the thorax. The low-dose CT is advised as the best lung screening test. Medical experts can recognize the disease-related lung nodules using these scans. However, the demand outpaces the number of radiologists who review lung scans. Artificial Intelligence algorithms have been utilized in several researches to find lung nodules in chest CT scans. It is challenging to analyze the models scientifically since they utilize multiple models on different datasets and the results are evaluated using various benchmarks, such as sensitivity, specificity, and accuracy.
Work in the field begins to appear
AI software for lung nodule evaluation utilizing deep learning (DL) could be able to recognize certain patterns in imaging data and provide outstanding results, according to research published in the journal Radiology. In order to train a DL algorithm to predict the malignancy risk of lung nodules, researchers from the Netherlands analyzed CT images of more than 16,000 nodules from the National Lung Cancer Screening Trial. The scans weren’t only recent; they also date back to before the patients’ lung cancer diagnoses.
In the study “End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography”, an AI system detected nodules in patient images more precisely than skilled radiologists did. Similar to other systems, this one used Deep Learning methods to find lung nodules in CT images.
To train the system, the researchers used a collection of over 40,000 CT images. The computer developed a better ability to recognize early cancer signs as it learnt which visual characteristics set apart malignant from benign patches. In diagnosing lung cancer in its early stages, it surpassed a group of six experienced radiologists 94% of the time.
What are the difficulties of implementing AI?
The future of AI applications in lung cancer could focus on integration and applications. First, because AI is a data-driven technology, scientist can integrate small datasets to create large data sets for training. However, regulations regarding data sharing are a huge obstacle for researchers. Federated learning, which distributes the trained parameters as opposed to the original data, might be the answer. In federated learning, the models are trained independently at many hospitals, and only the final algorithms are delivered to the main server, protecting the raw data.
With AI performing some hard work, lung cancer screening programs may save many lives—at a lower cost—without putting as much strain on radiologists. Therefore, reducing the number of unnecessary diagnostic procedures, the cost of lung cancer screening, and radiologists’ workload is a possible benefit of using the AI models.
Even though it is still in its infancy, this method has the ability to have a big impact on lung cancer diagnosis in the future. It will add to the expanding body of research that supports the use of AI in global medical developments and pave the way for clinical trials of novel treatments.
AI is set to enter a critical new phase in radiology, thanks to a growing body of research. Researchers have so far worked on how to identify specific problems before building, training, testing and validating AI models in different populations.
Now begins the most crucial step, which is still in its early stages worldwide: thorough evaluation once the model has been clinically implemented. This assessment should include tried-and-true approaches like the sensitivity rate for detecting cancers and the percentage of false-positive results. The future will be interesting. Join us and be a part of it.