Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska, Joanna; Borak, Martyna

doi:10.3390/s22218326

Cited by 7 publications

(3 citation statements)

References 111 publications

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“… 24 Computer-aided diagnosis (CAD) can improve the detection rate of focus areas and reduce the false-negative rate by locating abnormal or suspicious areas in images. 25 In CAD, machine learning methods are used to analyse imaging and non-imaging data from past case samples of patient populations and to develop a model to correlate extracted information with certain disease outcomes. As data from the new case is entered, the developed model is expected to predict the outcome of the new unknown case.…”

Section: Artificial Intelligence-assisted Thyroid Ultrasound Diagnosi...mentioning

confidence: 99%

AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis

Yao¹,

Zhang²,

Fei³

et al. 2023

JMDH

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The prevalence of antibody-negative chronic autoimmune thyroiditis (SN-CAT) is increasing. The early diagnosis of SN-CAT can effectively prevent its further development. Thyroid ultrasound can diagnose autoimmune thyroiditis and predict hypothyroidism. Primary hypothyroidism with a hypoechoic pattern suggested by thyroid ultrasound and negative thyroid serum antibodies is the main basis for the diagnosis of SN-CAT. However, for early SN-CAT, only hypoechoic thyroid changes and serological antibodies are currently available. This study explored how to achieve an accurate and early diagnosis of SN-CAT and prevent the development of SN-CAT combined with hypothyroidism. The diagnosis of a hypoechoic thyroid by artificial intelligence is expected to be a breakthrough in the accurate diagnosis of SN-CAT.

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Section: Artificial Intelligence-assisted Thyroid Ultrasound Diagnosi...mentioning

confidence: 99%

AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis

Yao¹,

Zhang²,

Fei³

et al. 2023

JMDH

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Section: Introductionmentioning

confidence: 99%

“…Medical imaging systems play an important role in disease diagnosis, staging, treatment selection, planning, guidance, and follow-up [ 1 , 2 , 3 ]. Ultrasound (US) imaging is a diagnostic imaging modality that is widely used because of its advantages of being non-invasive, non-ionizing, and painless [ 1 , 2 ]. US imaging systems are also more portable and provide more real-time feedback than other imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT).…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Image Registration Using Accelerated CNN

Aydin

Bilge

2023

Sensors

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Background: Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN applications. Methods: This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation. Results: The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency. Conclusions: Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.

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Introduction to computer-aided diagnosis (CAD) tools and applications

Prakash,

Gupta

2024

Advances in Computers

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Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Cited by 7 publications

References 111 publications

AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis

AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis

FPGA Implementation of Image Registration Using Accelerated CNN

Introduction to computer-aided diagnosis (CAD) tools and applications

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