A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions

Shrestha, Pallabi; Poudyal, Bhavya; Yadollahi, Sepideh; Wright, D. E.; Gregory, Adriana; Warner, Joshua D.; Korfiatis, Panagiotis; Green, Isabel C.; Rassier, Sarah L. Cohen; Mariani, Andrea; Kim, Bohyun; Laughlin-Tommaso, Shannon K.; Kline, Timothy L.

doi:10.1016/j.ygyno.2022.07.024

Cited by 41 publications

(19 citation statements)

References 45 publications

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“…Radiomics applied to MRI and artificial intelligence are valuable tools that can aid clinicians in the proper diagnosis and management of EC [ 64 ]. These tools have shown promising results in other gynecological tumors as well [ 65 , 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis

Madár,

Szabó,

Vleskó

et al. 2024

Cancers

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In endometrial cancer (EC), deep myometrial invasion (DMI) is a prognostic factor that can be evaluated by various imaging methods; however, the best method of choice is uncertain. We aimed to compare the diagnostic performance of two-dimensional transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) in the preoperative detection of DMI in patients with EC. Pubmed, Embase and Cochrane Library were systematically searched in May 2023. We included original articles that compared TVS to MRI on the same cohort of patients, with final histopathological confirmation of DMI as reference standard. Several subgroup analyses were performed. Eighteen studies comprising 1548 patients were included. Pooled sensitivity and specificity were 76.6% (95% confidence interval (CI), 70.9–81.4%) and 87.4% (95% CI, 80.6–92%) for TVS. The corresponding values for MRI were 81.1% (95% CI, 74.9–85.9%) and 83.8% (95% CI, 79.2–87.5%). No significant difference was observed (sensitivity: p = 0.116, specificity: p = 0.707). A non-significant difference between TVS and MRI was observed when no-myometrium infiltration vs. myometrium infiltration was considered. However, when only low-grade EC patients were evaluated, the specificity of MRI was significantly better (p = 0.044). Both TVS and MRI demonstrated comparable sensitivity and specificity. Further studies are needed to assess the presence of myometrium infiltration in patients with fertility-sparing wishes.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis

Madár,

Szabó,

Vleskó

et al. 2024

Cancers

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“…Artificial intelligence (AI) has recently become a more common and easily applied tool in medical science. Some AI applications involve medical imaging [ 11 , 12 , 13 , 14 , 15 ], though some do not [ 16 , 17 , 18 , 19 ]. Machine learning is part of the concept of AI and can be used to acquire rules for judging unknown data by learning the latent patterns in the data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria

Miyagi,

Tada,

Yasuhi

et al. 2024

JCM

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(1) Background: Although the diagnostic criteria for massive hemorrhage with organ dysfunction, such as disseminated intravascular coagulation associated with delivery, have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with >2000 mL of blood loss, among 13,368 deliveries, was obtained from nine national perinatal centers in Japan between 2020 and 2023. Twenty-three patients had fibrinogen levels <170 mg/dL, which is the initiation of coagulation system failure, according to our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. A positive FDP–fibrinogen/3–60 (mg/dL) value indicates hematuria; otherwise, the case is nonhematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: Using artificial intelligence, the borderline criterion was obtained, which divides the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery; this method appears to be useful.

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“…Gynecological cancers (GCs), primarily comprising ovarian cancer (OC), endometrial cancer (EC, also known as uterine cancer), and cervical cancer (CC), present a significant global public health concern with profound implications for women's health and quality of life, leading to substantial disease burden. The high mortality rate and poor prognosis associated with these cancers have imposed significant pressure for effective prevention and management strategies [1] . According to the 2020 global cancer statistics, the new cases of OC, CC and EC were 313,959, 604,127, and 417,367, the new deaths were 207,252, 341,831, and 97,370, respectively [2] .…”

Section: Introductionmentioning

confidence: 99%

Role of artificial intelligence in digital pathology for gynecological cancers

Wang,

Gao,

Xiao

et al. 2024

Computational and Structural Biotechnology Journal

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A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions

Cited by 41 publications

References 45 publications

Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis

Diagnostic Accuracy of Transvaginal Ultrasound and Magnetic Resonance Imaging for the Detection of Myometrial Infiltration in Endometrial Cancer: A Systematic Review and Meta-Analysis

A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria

Role of artificial intelligence in digital pathology for gynecological cancers

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