Technological Evolution of Ultrasound Devices: A Review

Nayak, Gayathri; Bolla, Vallidevi; Balivada, Santosh K.; P.,  Prabhudev

doi:10.60142/ijhti.v1i03.55

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Most tumors can also be detected, such as breast cancer, thyroid cancer, liver cancer, pancreatic cancer, bladder cancer, kidney cancer, ovarian cancer, etc. [96][97][98]. Still, B-ultrasound cannot detect some cancers, such as bone tumors, gastrointestinal tumors, and lung tumors.…”

Section: Ultrasoundmentioning

confidence: 99%

“…Safe and effective delivery of anticancer drugs to lesions is one of the grand challenges in cancer therapy. An ultrasound-triggered drug delivery system (UTDDS) was developed into a new type of efficient and non-invasive drug delivery technology [97,100], such as using ultrasound to break through the blood-brain barrier to deliver drugs to brain tumors [101]. It can position ultrasound radiation in the tumor area and promote the regular and quantitative release of the drug through ultrasound stimulation to achieve a high concentration of local chemotherapy drugs and improve the anti-cancer effect.…”

Section: Ultrasoundmentioning

confidence: 99%

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Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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Section: Ultrasoundmentioning

confidence: 99%

Section: Ultrasoundmentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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“…The prognosis of a CHD is difficult using fetal ultrasound (US), since US imaging has the potential to create blurry boundaries (shadows), which affect the image quality and its accuracy (Brady et al, 2023;Jiang et al, 2023). As the healthcare practitioner acquires images, the process of echocardiography is dependent not only on the ability to capture images, but also on the ability to analyze those images using a highly developed pattern recognition technique (Picano et al, 2023;Nayak et al, 2022). Extracting information from ultrasound (US) images is more complicated and computationally demanding challenge (Ricci et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images

Magesh,

Kumar

2023

JAETS

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Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD. To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.

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A Product Evaluation of Augmented Reality Equipment Coupled to Diagnostic Medical Sonography: A Potential Equipment and Ergonomic Innovation

Evans,

Bradbury,

Bloom

et al. 2024

Journal of Diagnostic Medical Sonography

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Objective: The aim of this product evaluation was to investigate the workflow and occupational health benefits of implementing augmented reality (AR) technology (MediView XR Inc, Cleveland, OH) paired with a portable ultrasound equipment system. Materials and Method: A diverse sample of sonography users was consented and ranged in experience levels. They were asked to complete a video training session, a hands-on demonstration, and remote instruction while using the augmented reality ultrasound (AR US) system. The duration of participants’ tasks was assessed during the use of both AR US and the 2-dimensional ultrasound (2D US) system. Participants’ postures and positions were evaluated using the Rapid Upper Limb Assessment (RULA), which assessed ergonomic risk factors, linked to body position and muscle usage. Participants also completed the System Usability Scale (SUS). Finally, participants were invited to complete a qualitative interview session, regarding the use of the AR US system. Results: A total of 20 participants provided data after using both the AR US and 2D US systems. The assessment data revealed positive SUS scores, which reflected favorably on both the product, education, and the training provided. The time spent on simulated work (20 minutes), with both imaging systems, remained consistent. The Rapid Upper Limb Assessment tool (RULA) scores for the cohort indicated a lower (better) median upper body score when using the AR US system. The cohort also had a statistically significant lower median subscore for the neck, trunk, and legs, compared with 2D US ( p < 0.00). Conclusion: Although larger studies are necessary to validate these findings, this cohort identified several opportunities for integrating the AR US system. Preliminary evidence suggests that an AR US system may offer occupational health benefits, as part of the imaging workflow.

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Technological Evolution of Ultrasound Devices: A Review

Cited by 4 publications

References 32 publications

Deep Learning for Medical Image-Based Cancer Diagnosis

Deep Learning for Medical Image-Based Cancer Diagnosis

Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images

A Product Evaluation of Augmented Reality Equipment Coupled to Diagnostic Medical Sonography: A Potential Equipment and Ergonomic Innovation

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