Advantages and Limitations of Ultrasound as a Screening Test for Ovarian Cancer

Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD)

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

Is three-dimensional ultrasonography a valuable diagnostic tool for patients with ovarian cancer? Systematic review and meta-analysis

Introduction

This paper was to assess the diagnostic performance and clinical value of three-dimensional ultrasonography (3DUS), three-dimensional ultrasonography power Doppler (3DPD), and 3DUS combined with 3DPD in ovarian cancer (OC).

Methods

The study was registered with PROSPERO (CRD 42023405765). PubMed and Web of Science were searched from inception to 25 January 2022, and reference lists of potentially eligible studies were also manually searched. Patient and study characteristics were extracted by two independent reviewers. Any discrepancies were addressed through discussion. The sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR, respectively), and the area under the receiver operating characteristic curve (AUC) were pooled separately.

Results

We retrieved 2,566 studies, of which 18 were finally enrolled, with 2,548 cases. The pooled sensitivity, specificity, PLR, NLR, and AUC for 3DUS were 0.89 (95% CI: 0.85-0.93), 0.93 (95% CI: 0.88-0.96), 13.1 (95% CI: 7.3-23.4), 0.11 (95% CI: 0.08-0.16), and 0.90 (95% CI: 0.87-0.93), respectively. The pooled sensitivity, specificity, PLR, NLR, and AUC for 3DPD were 0.90 (95% CI: 0.80-0.95), 0.85 (95% CI: 0.71-0.92), 5.8 (95% CI: 3.0-11.2), 0.12 (95% CI: 0.06-0.24), and 0.94 (95% CI: 0.91-0.96), respectively. The pooled sensitivity, specificity, PLR, NLR, and AUC for 3DUS combined with 3DPD were 0.99 (95% CI: 0.73-1.00), 0.95 (95% CI: 0.85-0.99), 21.9 (95% CI: 6.1-78.9), 0.01 (95% CI: 0.00-0.37), and 0.99 (95% CI: 0.98-1.00), respectively.

Conclusions

3DUS, 3DPD, and 3DUS combined with 3DPD are promising diagnostic tools for OC, alongside elevated sensitivity and specificity. However, the combination of 3DUS and 3DPD techniques has higher diagnostic efficiency.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD 42023405765.

Ultrasound Image Analysis with Vision Transformers-Review

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.