CONTENTS
Review Articles
- Update on the Genetics and Prenatal Ultrasound Features of Williams-Beuren Syndrome
- Shanqing Li, MM, Rong Hu, MM, Xijing Liu, MD, Fan Yang, MD
- 2024, 8 (3): 79-85. DOI:10.37015/AUDT.2024.240036
- Abstract ( 145 ) HTML ( 12 ) PDF ( 1191KB ) ( 125 )
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A heterozygous microdeletion of chromosome 7q11.23 causes the rare neuropsychiatric developmental disorder Williams-Beuren Syndrome. The syndrome is more difficult to diagnose before birth than after, even though the syndrome often manifests prenatally as intrauterine growth restriction and cardiovascular defects on prenatal ultrasonography. The potentially poor prognosis of affected individuals highlights the need to improve prenatal diagnosis of the syndrome. This review summarizes recent advances in our understanding of the genetics of Williams-Beuren Syndrome and its manifestations on prenatal ultrasonography, which may facilitate its early detection and inform prenatal genetic counseling.
- Deep Learning in Ultrasound Localization Microscopy
- Yuhang Zheng, BS, Jianqiao Zhou, MD
- 2024, 8 (3): 86-92. DOI:10.37015/AUDT.2024.240023
- Abstract ( 114 ) HTML ( 4 ) PDF ( 3028KB ) ( 70 )
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Ultrasound imaging holds a significant position in medical diagnostics due to its non-invasive and real-time capabilities. However, traditional ultrasound is constrained by the diffraction limit, making it challenging to capture fine blood vessels. Ultrasound localization microscopy (ULM) overcomes this limitation by achieving super-resolution imaging through tracking the trajectories of microbubbles (MBs) within microvasculature. This review summarizes the applications of deep learning (DL) techniques in ULM post-processing algorithms, including key steps such as beamforming, clutter filtering and denoising, localization, and tracking. Although DL shows great potential in improving ULM imaging quality and efficiency, current research mainly focuses on imaging algorithmic improvements rather than in-depth image analysis. In the future, with the accumulation of ULM image data, the powerful feature extraction capability of DL is expected to further advance ULM applications in disease prediction and diagnosis.
- Advances in Deep Learning-Based Ultrasound Microscopy of Microvasculature: Basic and Clinical Research
- Ji-Bin Liu, MD, FAIUM, Editor-in-Chief, AUDT
- 2024, 8 (3): 93-93. DOI:10.37015/AUDT.2024.240026
- Abstract ( 116 ) HTML ( 11 ) PDF ( 216KB ) ( 54 )
- Deep Learning for Segmentation and Classification in Mammograms for Breast Cancer Detection: A Systematic Literature Review
- Raymond Sutjiadi, MS, Siti Sendari, PhD, Heru Wahyu Herwanto, PhD, Yosi Kristian, PhD
- 2024, 8 (3): 94-105. DOI:10.37015/AUDT.2024.230051
- Abstract ( 111 ) HTML ( 7 ) PDF ( 1334KB ) ( 51 )
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Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computer-aided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection.
Original Research
- Ultrasound-based Dual Elastography for Evaluating the Severity of Drug-induced Liver Injury: One Step Closer to Pathology
- Liyun Xue, PhD, Hui Feng, MD, Fankun Meng, MD, Ying Zheng, MD, Guangwen Cheng, PhD, Yao Zhang, MD, Zhiyong Yin, MD, Jing Wu, MD, Jiabao Zhu, MD, Xueqi Li, MD, Jie Yu, PhD, Ping Liang, PhD, Hong Ding, PhD
- 2024, 8 (3): 106-115. DOI:10.37015/AUDT.2024.240017
- Abstract ( 81 ) HTML ( 7 ) PDF ( 1086KB ) ( 37 )
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Objective: Drug-induced liver injury (DILI) is one of the most challenging forms of liver disorder. We aimed to use ultrasound dual elastography, by combining strain and shear wave imaging, to noninvasively assess liver inflammation and injury severity of DILI.
Methods: 291 DILI patients were included in the prospective multicenter study and divided into training and validation cohorts. All patients received liver biopsy and dual elastography examination. Liver inflammation grading (G0-4) and fibrosis staging (F0-4) were considered as the gold standard of liver injury and G+F ≥ 5 was defined as severe liver injury. Indexes of dual elastography and serological indicators (DESI) were selected and analyzed with multivariable logistic regression to build DESI models for evaluating liver inflammation, and the C score model was built with the same method for diagnosing severe liver injury.
Results: Areas under the receiver operating characteristic curve (AUCs) of the DESI model to assess liver inflammation ≥ G2 were 0.887 and 0.868 in training and validation cohorts, respectively. AUCs of the DESI model in diagnosing ≥ G3 were 0.893 and 0.896 in the two cohorts, respectively. The C score accurately assessed severe liver injury with AUCs of 0.909 and 0.885 in two cohorts. Of the 87 patients with mild clinical severity, 10 (11.49%) had severe pathological injury, which could be identified by C score.
Conclusion: Dual elastography demonstrated high performance in diagnosing liver inflammation and identifying severe pathological liver injury of DILI, making up for the deficiency of serological indicators alone for evaluating DILI severity.
- Can Different Expertise Levels of Ultrasound Operators Accurately Screen with Handheld Ultrasound?
- Yuzhou Shen, MD, Lin Jin, MD, Lei Sha, MD, Mengmeng Cao, MD, Desheng Sun, MD, Li Liu, MD, Zhaojun Li, MD
- 2024, 8 (3): 116-123. DOI:10.37015/AUDT.2023.230046
- Abstract ( 86 ) HTML ( 4 ) PDF ( 1362KB ) ( 32 )
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Objective: To evaluate accuracy and feasibility of a handheld ultrasound machine for measuring carotid artery intima-media thicknesses (CIMT) and hemodynamic parameters by different expertise levels of ultrasound operators.
Methods: The operators were divided into three groups based on the level of their medical expertise: ultrasound technician, sonographer, and nursing staff. Operators from each group measured the CIMT and hemodynamic parameters of 25 volunteers using both handheld ultrasound and a conventional ultrasound machine. The reliability and reproducibility of handheld ultrasound measurements of CIMT and hemodynamic parameters (peak systolic velocity (PSV), end-diastolic velocity (EDV)) in operators were analyzed.
Results: After a period of training, there was no statistically significant difference between the mean CIMT measured using handheld ultrasound among the three operators (0.45 ± 0.09 mm, 0.50 ± 0.07 mm, 0.46 ± 0.08 mm, P> 0.05, respectively), as well as PSV (83.30 ± 15.42 cm/s, 76.28 ± 13.26 cm/s, 81.12 ± 21.21 cm/s, P> 0.05, respectively) and EDV (21.04 ± 4.12 cm/s, 21.87 ± 5.05 cm/s, 20.17 ± 5.90 cm/s, respectively, P> 0.05). Furthermore, there was a good repeatability and consistent of handheld ultrasound device in measuring mean CIMT in the ultrasound technician and sonographer groups (r = 0.662, 0.691, respectively, P < 0.01).
Conclusions: Under the premise of proper training, handheld ultrasound systems are feasible for rapid and primary assessment of carotid artery by operators with different levels of expertise.
- The Diagnostic Value of Real-time Shear Wave Ultrasound Elastography in the Differentiation of Hepatic Hemangioma and Hepatocellular Carcinoma
- Yue Tian, MB, Yanan Zhao, MD, Yiran Huang, MB
- 2024, 8 (3): 124-129. DOI:10.37015/AUDT.2023.230057
- Abstract ( 88 ) HTML ( 11 ) PDF ( 1310KB ) ( 63 )
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Objective: To explore the diagnostic value of real-time shear wave elastography (SWE) in hepatic hemangioma (ANGI) and hepatocellular carcinoma (HCC).
Methods: A total of 88 patients diagnosed with angiosarcoma (ANGI) or hepatocellular carcinoma (HCC) in our department from February 2019 to February 2020 were included in this study. These patients were assigned to two groups based on pathological diagnosis: the ANGI group (n = 42) and the HCC group (n = 46). All patients underwent SWE examination, and the clinical efficacy was analyzed by comparing the average value of Young's modulus of the two groups and plotting the receiver operator characteristic (ROC) curve.
Results: There was no significant difference in lesion diameter between ANGI and HCC patients (P > 0.05). The ANGI group exhibited a significantly higher average Young's modulus for both the lesions and the adjacent liver tissues compared to the HCC group (P < 0.001); The area under the curve (AUC) of the average value of Young’s modulus of the lesion to ANGI and HCC was 0.875 (95% CI = 0.795-0.955).
Conclusion: SWE demonstrates high diagnostic accuracy in distinguishing ANGI from HCC, providing valuable clinical evidence for differentiating between the two diseases.
- Does Covid-19 Cause An Increase in Spleen Dimensions? Ultrasonography Study in People with Recent History of COVID-19 Infection and Healthy Participants
- Syed Muhammad Yousaf Farooq, PhD, Syed Amir Gilani, PhD, Rabia Ejaz, BS, Sheeza Fatima, BS, Sarosh Imran, BS, Aleeza Naseer, BS
- 2024, 8 (3): 130-134. DOI:10.37015/AUDT.2023.230056
- Abstract ( 90 ) HTML ( 3 ) PDF ( 1159KB ) ( 25 )
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Objective: To determine whether COVID-19 causes an increase in spleen dimensions in individuals with a recent history of COVID-19 infection.
Methods: This case-control study was conducted at the Radiology Department of the University of Lahore Teaching Hospital and Sehat Medical Complex, both in Lahore. The study sample comprised 384 individuals, selected using a convenience sampling technique. Participants included individuals of all age groups and both genders; however, those under 18 were excluded due to the potential for incomplete spleen maturation. Other exclusion criteria included a history of splenectomy, the presence of traumatic or non-traumatic splenic lesions, or any other splenic abnormalities. Data collection commenced after obtaining approval from the Research Ethics Committee at the University of Lahore. The Siemens Sonovista c3000 Grey Scale Ultrasound Machine was used, and the data were analyzed using SPSS version 24.
Results: In a study involving 384 participants, the mean age was 35.7 ± 6.14, ranging from 22 to 50 years. Of these, 296 (71.1%) were female, and 88 (22.9%) were male. Echogenicity varied, with 29 participants (7.6%) exhibiting heterogeneous echogenicity and 355 (92.4%) showing homogeneous echogenicity. Spleen margins were irregular in 67 participants (17.4%) and smooth in 317 participants (82.6%). Regarding the history of Covid-19, 188 participants (49%) tested negative, while 196 participants (51%) tested positive.
Conclusion: Patients with a history of COVID-19 exhibited a significant increase in spleen length, volume, and thickness.