Abstract:Radiologic imaging is crucial for diagnosing and monitoring rheumatic inflammatory diseases. Particularly the emerging approach of precision medicine has increased the interest in quantitative imaging. Extensive research has shown that ultrasound allows a quantification of direct signs such as bone erosions and synovial thickness. Dual-energy X-ray absorptiometry and high-resolution peripheral quantitative computed tomography (CT) contribute to the quantitative assessment of secondary signs such as osteoporosi… Show more
“…This technology has improved the routine ultrasound examination. After intravenous injection of ultrasound contrast agent [ 25 ], the ability to detect tissue blood perfusion is enhanced. The microvascular structure of normal and diseased tissue can be clearly displayed, thereby improving the accuracy of doctors' diagnosis.…”
To discuss the optimal interval time between genetic algorithm-based ultrasound imaging-guided percutaneous drainage surgery (PTGD) and laparoscopic cholecystectomy (LC), 64 cholecystitis patients were selected as the research objects and evenly divided into experimental group (intelligent algorithm was adopted to recognize patients’ ultrasonic images) and control group (professional doctors carried out diagnosis). 92 acute cholecystitis patients undergoing PTGD were divided into three groups. 30 out of the 92 patients received LC within 2 months and were defined as the early group. 32 were performed with LC within 2 to 4 months and were defined as the metaphase group. 28 underwent LC over 4 months and were defined as the late-stage group. The average operation time, the transition from LC to laparotomy, the average postoperative hospital stay, and the incidence of complications of the three groups were compared. The results revealed that the comparison of the diagnostic accuracy and comprehensive effectiveness between experimental group and control group demonstrated that the differences were statistically significant (
P
<
0.05
). When the optimal interval of implementing LC after PTGD was realized, the corresponding values of the early group were 88.5 minutes, 16.67%, 8.13 days, and 13.75%. Those of the metaphase group were 49.91 minutes, 3.13%, 4.97 days, and 9.52%. Those of the late stage group were 68.78 minutes, 10.71%, 7.09 days, and 11.96%. To sum up, the diagnostic accuracy and comprehensive effectiveness of intelligent algorithm were higher than those of conventional ultrasound, and the optimal interval time of implementing LC after PTGD was 2 to 4 months.
“…This technology has improved the routine ultrasound examination. After intravenous injection of ultrasound contrast agent [ 25 ], the ability to detect tissue blood perfusion is enhanced. The microvascular structure of normal and diseased tissue can be clearly displayed, thereby improving the accuracy of doctors' diagnosis.…”
To discuss the optimal interval time between genetic algorithm-based ultrasound imaging-guided percutaneous drainage surgery (PTGD) and laparoscopic cholecystectomy (LC), 64 cholecystitis patients were selected as the research objects and evenly divided into experimental group (intelligent algorithm was adopted to recognize patients’ ultrasonic images) and control group (professional doctors carried out diagnosis). 92 acute cholecystitis patients undergoing PTGD were divided into three groups. 30 out of the 92 patients received LC within 2 months and were defined as the early group. 32 were performed with LC within 2 to 4 months and were defined as the metaphase group. 28 underwent LC over 4 months and were defined as the late-stage group. The average operation time, the transition from LC to laparotomy, the average postoperative hospital stay, and the incidence of complications of the three groups were compared. The results revealed that the comparison of the diagnostic accuracy and comprehensive effectiveness between experimental group and control group demonstrated that the differences were statistically significant (
P
<
0.05
). When the optimal interval of implementing LC after PTGD was realized, the corresponding values of the early group were 88.5 minutes, 16.67%, 8.13 days, and 13.75%. Those of the metaphase group were 49.91 minutes, 3.13%, 4.97 days, and 9.52%. Those of the late stage group were 68.78 minutes, 10.71%, 7.09 days, and 11.96%. To sum up, the diagnostic accuracy and comprehensive effectiveness of intelligent algorithm were higher than those of conventional ultrasound, and the optimal interval time of implementing LC after PTGD was 2 to 4 months.
“…In the last few decades, novel MRI techniques have emerged: a robust fat suppression (FS) technique using chemical shift imaging (CSI), diffusion-weighted imaging (DWI), advanced quantitative MRI techniques, such as T2 mapping and T1ρ mapping, dynamic contrast-enhanced MRI (DCE-MRI), diffusion tensor imaging, and whole-body MRI (WB-MRI). [4][5][6] Robust FS techniques predominantly involve Dixon sequences that use CSI. They take advantage of the slight difference in resonance frequency between water and fat protons to provide a series of four sets of images: in-phase (IP), out-of-phase (OP), water only, and fat only.…”
Section: Magnetic Resonance Imagingmentioning
confidence: 99%
“…It has not gained popularity, despite reports of comparable accuracy in diagnosing synovitis and predicting radiologic and clinical response in patients with JIA. [6][7][8] Some challenges associated with this technique include long study time, high sensitivity to field inhomogeneities, the need for strong gradients, and subjectivity in positioning the ROIs that hampers the objective quantification of disease. 6 Nevertheless, some potential solutions to resolve this shortcoming have already been proposed.…”
The knee is one of the most commonly affected joints in the course of inflammatory arthropathies, such as crystal-induced and autoimmune inflammatory arthritis. The latter group includes systemic connective tissue diseases and spondyloarthropathies. The different pathogenesis of these entities results in their varied radiologic images. Some lead quickly to joint destruction, others only after many years, and in the remaining, destruction will not be a distinguishing radiologic feature.Radiography, ultrasonography, and magnetic resonance imaging have traditionally been the primary modalities in the diagnosis of noninflammatory and inflammatory arthropathies. In the case of crystallopathies, dual-energy computed tomography has been introduced. Hybrid techniques also offer new diagnostic opportunities. In this article, we discuss the pathologic findings and imaging correlations for crystallopathies and inflammatory diseases of the knee, with an emphasis on recent advances in their imaging diagnosis.
“…Until a few years ago, the phenotypic information was not considered big ( 19 ), but with the evolution in terms of standardization and FAIRness, the consequent simplification in data merged across healthcare providers, and the integration among different data sources transformed clinical data into new types of big files. The primary and essential investigation of skeletal disorders is imaging data, ranging from traditional X-Rays and ultrasounds, through hybrid imaging such as positron emission tomography/MRI (PET/MRI) up to innovative instruments like high-resolution peripheral quantitative CT (HR-pQCT) ( 20 ). These data are increasingly needed to support the diagnostic process, to longitudinally follow-up disease evolution, and to promote translational research.…”
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.
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