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The Validity of Ultrasound Technology in Providing an Indirect Estimate of Muscle Glycogen Concentrations Is Equivocal.
Bone, JL, Ross, ML, Tomcik, KA, Jeacocke, NA, McKay, AKA, Burke, LM
Nutrients. 2021;(7)
Abstract
Researchers and practitioners in sports nutrition would greatly benefit from a rapid, portable, and non-invasive technique to measure muscle glycogen, both in the laboratory and field. This explains the interest in MuscleSound®, the first commercial system to use high-frequency ultrasound technology and image analysis from patented cloud-based software to estimate muscle glycogen content from the echogenicity of the ultrasound image. This technique is based largely on muscle water content, which is presumed to act as a proxy for glycogen. Despite the promise of early validation studies, newer studies from independent groups reported discrepant results, with MuscleSound® scores failing to correlate with the glycogen content of biopsy-derived mixed muscle samples or to show the expected changes in muscle glycogen associated with various diet and exercise strategies. The explanation of issues related to the site of assessment do not account for these discrepancies, and there are substantial problems with the premise that the ratio of glycogen to water in the muscle is constant. Although further studies investigating this technique are warranted, current evidence that MuscleSound® technology can provide valid and actionable information around muscle glycogen stores is at best equivocal.
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A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.
Zhang, D, Jiang, F, Yin, R, Wu, GG, Wei, Q, Cui, XW, Zeng, SE, Ni, XJ, Dietrich, CF
Medical science monitor : international medical journal of experimental and clinical research. 2021;:e931957
Abstract
Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.
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Radioproteomics in patients with ovarian cancer.
McCague, C, Beer, L
The British journal of radiology. 2021;(1125):20201331
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Abstract
Radioproteomics is the integration of proteomics, the systematic study of the protein expression of an organism, with radiomics, the extraction and analysis of large numbers of quantitative features from medical images. This article examines this developing field, and it's application in high grade serous ovarian carcinoma. Seminal proteomic studies in the area of ovarian cancer, such as the PROVAR and CPTA studies are discussed, along side recent research, such as that highlighting the central role of methyltransferase nicotinamide N-methyltransferase as the metabolic regulation of cancer progression in the tumour stroma. Finally, this article considers a novel, hypothesis generating approach to integrate CT-based qualitative and radiomic features with proteomic analysis, and the future direction of the field. Combined advances in radiomic, proteomic and genomic analysis has the potential to signal the age of true precision medicine, where treatment is centered specifically on the molecular profile of the tumour, rather than based on empirical knowledge, thus altering the course of a disease that has the highest mortality of all cancers of the female reproductive system.
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Skeletal ciliopathies: a pattern recognition approach.
Handa, A, Voss, U, Hammarsjö, A, Grigelioniene, G, Nishimura, G
Japanese journal of radiology. 2020;(3):193-206
Abstract
Ciliopathy encompasses a diverse group of autosomal recessive genetic disorders caused by mutations in genes coding for components of the primary cilia. Skeletal ciliopathy forms a subset of ciliopathies characterized by distinctive skeletal changes. Common skeletal ciliopathies include Jeune asphyxiating thoracic dysplasia, Ellis-van Creveld syndrome, Sensenbrenner syndrome, and short-rib polydactyly syndromes. These disorders share common clinical and radiological features. The clinical hallmarks comprise thoracic hypoplasia with respiratory failure, body disproportion with a normal trunk length and short limbs, and severely short digits occasionally accompanied by polydactyly. Reflecting the clinical features, the radiological hallmarks consist of a narrow thorax caused by extremely short ribs, normal or only mildly affected spine, shortening of the tubular bones, and severe brachydactyly with or without polydactyly. Other radiological clues include trident ilia/pelvis and cone-shaped epiphysis. Skeletal ciliopathies are commonly associated with extraskeletal anomalies, such as progressive renal degeneration, liver disease, retinopathy, cardiac anomalies, and cerebellar abnormalities. In this article, we discuss the radiological pattern recognition approach to skeletal ciliopathies. We also describe the clinical and genetic features of skeletal ciliopathies that the radiologists should know for them to play an appropriate role in multidisciplinary care and scientific advancement of these complicated disorders.
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Ophthalmic diagnosis using deep learning with fundus images - A critical review.
Sengupta, S, Singh, A, Leopold, HA, Gulati, T, Lakshminarayanan, V
Artificial intelligence in medicine. 2020;:101758
Abstract
An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy are also discussed. Important critical insights and future research directions are given.
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Fundamentals of artificial intelligence for ophthalmologists.
Ahmad, BU, Kim, JE, Rahimy, E
Current opinion in ophthalmology. 2020;(5):303-311
Abstract
PURPOSE OF REVIEW As artificial intelligence continues to develop new applications in ophthalmic image recognition, we provide here an introduction for ophthalmologists and a primer on the mechanisms of deep learning systems. RECENT FINDINGS Deep learning has lent itself to the automated interpretation of various retinal imaging modalities, including fundus photography and optical coherence tomography. Convolutional neural networks (CNN) represent the primary class of deep neural networks applied to these image analyses. These have been configured to aid in the detection of diabetes retinopathy, AMD, retinal detachment, glaucoma, and ROP, among other ocular disorders. Predictive models for retinal disease prognosis and treatment are also being validated. SUMMARY Deep learning systems have begun to demonstrate a reliable level of diagnostic accuracy equal or better to human graders for narrow image recognition tasks. However, challenges regarding the use of deep learning systems in ophthalmology remain. These include trust of unsupervised learning systems and the limited ability to recognize broad ranges of disorders.
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Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System.
Ashraf, MN, Hussain, M, Habib, Z
Current medical imaging. 2020;(4):397-426
Abstract
Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.
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APT-weighted MRI: Techniques, current neuro applications, and challenging issues.
Zhou, J, Heo, HY, Knutsson, L, van Zijl, PCM, Jiang, S
Journal of magnetic resonance imaging : JMRI. 2019;(2):347-364
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Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.
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Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Tang, A, Bashir, MR, Corwin, MT, Cruite, I, Dietrich, CF, Do, RKG, Ehman, EC, Fowler, KJ, Hussain, HK, Jha, RC, et al
Radiology. 2018;(1):29-48
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Abstract
The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.
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Thyroid Ultrasound: State of the Art. Part 2 - Focal Thyroid Lesions.
Dighe, M, Barr, R, Bojunga, J, Cantisani, V, Chammas, MC, Cosgrove, D, Cui, XW, Dong, Y, Fenner, F, Radzina, M, et al
Medical ultrasonography. 2017;(2):195-210
Abstract
Accurate differentiation of focal thyroid nodules (FTL) and thyroid abnormalities is pivotal for proper diagnostic and therapeutic work-up. In these two part articles, the role of ultrasound techniques in the characterization of FTL and evaluation of diffuse thyroid diseases is described to expand on the recently published World Federation in Ultrasound and Medicine (WFUMB) thyroid elastography guidelines and review how this guideline fits into a complete thyroid ultrasound exam.