Uterine bioimpedance combined with artificial intelligence as a means of cancer detection

Gupta, Shabnam; Vargas, Andrés; Saulnier, G.J.; Newell, J.C.; Faaborg-Andersen, Christian; Kelley, Robert S.

doi:10.1080/03091902.2021.1936674

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…A radiomic multivariable logistic regression model was proposed in 2019 by Xie et al, 43 yielding a 0.83 AUC, 76% sensitivity, and 73% specificity. Gupta et al 58 tested the performance of AI in the detection of uterine sarcoma through the analysis of bioimpedance, with an 80% overall accuracy. In 2019, Nakagawa et al 59 found that a multiparametric machine learning MRI‐based method had better results in terms of diagnosis of malignancy than positron emission tomography alone and was comparable to experienced radiologists.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Raffone,

Raimondo,

Neola

et al. 2023

Intl J Gynecology & Obste

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BackgroundDifferential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second‐line diagnostic method after ultrasound for the assessment of uterine masses.ObjectivesTo assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.Search StrategyA systematic review and meta‐analysis was performed searching five electronic databases from their inception to June 2023.Selection CriteriaAll peer‐reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.Data Collection and AnalysisSensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).ResultsEight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84–0.94), specificity of 0.96 (95% CI 0.96–0.97), positive likelihood ratio of 13.55 (95% CI 6.20–29.61), negative likelihood ratio of 0.08 (95% CI 0.02–0.32), diagnostic odds ratio of 175.13 (95% CI 46.53–659.09), and area under the curve of 0.9759.ConclusionsMRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

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Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Raffone,

Raimondo,

Neola

et al. 2023

Intl J Gynecology & Obste

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“…Several studies also use similar principal to measure tissue thickness. A study by Gupta et al also use marking to measure myometrial layer thickness for detection of uterine cancer [29]. After recognize the marker of each layer, our AI scan through the layer to find thickest part and measure pixels of C-IMT.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of cerebrovascular disease risk with carotid ultrasonography imaging in artificial intelligence framework

Danuaji

Subandi

Putra

et al. 2023

IJPHS

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Carotid plaque is a biomarker of generalized atherosclerosis, and may predict ischemic stroke. Carotid intima-media thickness (C-IMT) measurement with ultrasonography imaging could capture the condition of carotid plaque. However, manual measurement of C-IMT is observer- dependent, resulting in observer bias and low reproducibility. In this study, we develop artificial intelligence (AI) framework that could automatically measure the C-IMT, and compared it with C-IMT measured by board of expert. This is a retrospective study done in Dr. Moewardi General Hospital, Surakarta, Indonesia. Carotid B-mode ultrasonography images were measured by panel of expert and by AI. After annotation process on Neurabot platform, AI could detect region of interest (ROI), and would do segmentation on the area to measure C-IMT autonomously. Dependent T-test was used to evaluate validity, and Cronbach’s alpha was used to find the reliability of C-IMT measured by panel of expert and AI. There was strong correlation (r=0.874; p=0.014) on dependent t-test for C-IMT measured by AI with C-IMT measured by board of expert. The internal consistency reliability coefficients (Cronbach’s alpha) were 0.938 and 0.909, for pretest and posttest, respectively. We also analyzed the test-retest reliability by comparing pretest and posttest score with dependent t-test, and we observed strong correlation with r=0.871 (p=0.000). AI developed on Neurabot platform are valid and reliable to measure C-IMT.

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“…The impedance spectra of living tissues vary due to the differences in morphological structures and components. Therefore, the resistivity spectrum measured at each frequency is highly related to the physiological and pathological characteristics of the tissue [ 5 , 6 , 7 ]. Bioimpedance spectroscopy, a method of measuring electrical properties over a wide range of frequencies, can characterize target tissue.…”

Section: Introductionmentioning

confidence: 99%

Tissue Characterization Using an Electrical Bioimpedance Spectroscopy-Based Multi-Electrode Probe to Screen for Cervical Intraepithelial Neoplasia

Kang

Jung

et al. 2021

Diagnostics

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The successful management of cervical intraepithelial neoplasia (CIN) with proper screening and treatment methods could prevent cervical cancer progression. We propose a bioimpedance spectroscopic measurement device and a multi-electrode probe as an independent screening tool for CIN. To evaluate the performance of this screening method, we enrolled 123 patients, including 69 patients with suspected CIN and 54 control patients without cervical dysplasia who underwent a hysterectomy for benign disease (non-CIN). Following conization, the electrical properties of the excised cervical tissue were characterized using an electrical bioimpedance spectroscopy-based multi-electrode probe. Twenty-eight multifrequency voltages were collected through the two concentric array electrodes via a sensitivity-optimized measurement protocol based on an electrical energy concentration method. The electrical properties of the CIN and non-CIN groups were compared with the results of the pathology reports. Reconstructed resistivity tended to decrease in the CIN and non-CIN groups as frequency increased. Reconstructed resistivity from 625 Hz to 50 kHz differed significantly between the CIN and non-CIN groups (p < 0.001). Using 100 kHz as the reference, the difference between the CIN and non-CIN groups was significant. Based on the difference in reconstructed resistivity between 100 kHz and the other frequencies, this method had a sensitivity of 94.3%, a specificity of 84%, and an accuracy of 90% in CIN screening. The feasibility of noninvasive CIN screening was confirmed through the difference in the frequency spectra evaluated in the excised tissue using the electrical bioimpedance spectroscopy-based multi-electrode screening probe.

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Uterine bioimpedance combined with artificial intelligence as a means of cancer detection

Cited by 6 publications

References 20 publications

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Evaluation of cerebrovascular disease risk with carotid ultrasonography imaging in artificial intelligence framework

Tissue Characterization Using an Electrical Bioimpedance Spectroscopy-Based Multi-Electrode Probe to Screen for Cervical Intraepithelial Neoplasia

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