Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data. DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI. As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems.
The model is based on convolutional neural networks, a recent technique which have been proven to be very effective for various types of tasks. In particular, deep neural networks-based models outperform all previous approaches for image segmentation. However, 3D reconstruction from 2D images is still challenging for neural networks, due to the difficulty of representing a dimensional enlargement with standard differentiable layers. Reconstruction of bone surfaces in particular is extremely challenging, due to the transparent nature of the X-ray images. The main usage of the 3D model is for planning and accurate measurements that are needed to precise fit of the implant or for patient specific intraoperative guidance.
In the three minutes it will take you to read this blog post, a radiologist will review an estimated 45-60 images.1 And they must continue to review an image every 3-4 seconds during each eight-hour shift, five days a week, all year long. This pace is not sustainable, even for the most experienced radiologists working under ideal conditions. As image volume continues to increase, there simply aren't enough of us radiologists to make this formula work over the long term. We're struggling to balance productivity and quality, and it's contributing to physician burnout.
Background Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. One out of 2 women over the age of 50 years may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram.
Caption Health, a leading medical AI company, announced that its flagship product, Caption AI, the first AI-guided medical imaging acquisition system, is now available for pre-order by healthcare providers. Caption AI is a transformational new technology that enables healthcare practitioners--even those without prior ultrasound experience--with the ability to perform ultrasound exams quickly and accurately, by providing expert guidance, automated quality assessment, and intelligent interpretation capabilities. Caption AI comes equipped with Caption Guidance software, which uses artificial intelligence to provide real-time guidance and feedback on image quality to enable capture of diagnostic quality images. This announcement follows the recent groundbreaking marketing authorization of Caption Guidance software by the U.S. Food and Drug Administration (FDA). The safety and effectiveness of Caption Guidance was clinically validated in a multi-center prospective pivotal trial at Northwestern Medicine and Minneapolis Heart Institute at Allina Health with registered nurses with no prior ultrasound experience.
LucidHealth, a physician-owned and led radiology company, announced today that it is using an AI-powered diagnostic aid from leading AI vendor Aidoc to help prioritize and expedite treatment to patients with critical, life-threatening conditions. LucidHealth is one of the first radiology companies in the Midwest to incorporate artificial intelligence (AI) into its radiology practice, further cementing their commitment to continuously improving patient outcomes. "LucidHealth is committed to bringing the most advanced, highest quality technological solutions to assist our patients," said Mark Alfonso, M.D., chief medical officer, LucidHealth. "Aidoc's AI-powered alerting system combined with our own proprietary workflow software, RadAssist, enables us to prioritize the patients with the most urgent, time-critical, life-threatening conditions. For example, proactive examination for intracranial hemorrhages via AI automatically and immediately flags those cases to the radiologists, allowing them to prioritize and assist in addressing those patients sooner. This reduction in wait time could be life-altering; providing the ability to ensure rapid radiologist inspection and triage for expedited treatment."
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algorithms are selected, introduced, analyzed and summarized in detail. In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized. Further, the performance of the current methods and their evaluations are reviewed. In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.
AI can ensure that radiologists will generate exceptionally essential information to enhance the health of populaces and people. FREMONT, CA: Radiology has come to the surface as an innovator in artificial intelligence (AI) out of an extreme need. The yearning for more prominent efficacy and productivity in the field of clinical care has acted as an essential driver when it comes to the development of AI in medical imaging. The data from radiological imaging keeps developing at an irregular rate whenever compared. The quantity of the trained readers and the fall in imaging reimbursements has affected the healthcare suppliers, by remunerating the increasing efficiency.
Automatic radiology report generation has been an attracting research problem towards computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning techniques for natural image captioning are successfully adapted to generating radiology reports. However, radiology image reporting is different from the natural image captioning task in two aspects: 1) the accuracy of positive disease keyword mentions is critical in radiology image reporting in comparison to the equivalent importance of every single word in a natural image caption; 2) the evaluation of reporting quality should focus more on matching the disease keywords and their associated attributes instead of counting the occurrence of N-gram. Based on these concerns, we propose to utilize a pre-constructed graph embedding module (modeled with a graph convolutional neural network) on multiple disease findings to assist the generation of reports in this work. The incorporation of knowledge graph allows for dedicated feature learning for each disease finding and the relationship modeling between them. In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph. Experimental results demonstrate the superior performance of the methods integrated with the proposed graph embedding module on a publicly accessible dataset (IU-RR) of chest radiographs compared with previous approaches using both the conventional evaluation metrics commonly adopted for image captioning and our proposed ones.