Ultrasound in Obstetrics & Gynecology最新文献

{"title":"Re: Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.","authors":"G R DeVore","doi":"10.1002/uog.29158","DOIUrl":"10.1002/uog.29158","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"390-392"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical utility of prenatal exome sequencing for isolated short long bones and isolated small-for-gestational age.","authors":"B Jordan, S A Graham, S Allen, V Harrison","doi":"10.1002/uog.29188","DOIUrl":"10.1002/uog.29188","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"386-388"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delivering unexpected news in pregnancy: a call for specialized training.","authors":"I Karapanos, S Iliodromiti, E Greco","doi":"10.1002/uog.27682","DOIUrl":"10.1002/uog.27682","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"384"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence, radiomics and fetal ultrasound: review of literature and future perspectives.","authors":"A Bouachba, J De Jesus Neves, E Royer, R Bartin, L J Salomon, D Grevent, G Gorincour","doi":"10.1002/uog.29172","DOIUrl":"https://doi.org/10.1002/uog.29172","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":"65 3","pages":"281-291"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.","authors":"F Moro, M T Giudice, M Ciancia, D Zace, G Baldassari, M Vagni, H E Tran, G Scambia, A C Testa","doi":"10.1002/uog.29171","DOIUrl":"10.1002/uog.29171","url":null,"abstract":"<p><strong>Objective: </strong>Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the available evidence have been inadequate. The aim of this systematic review was to summarize and evaluate the literature on the role of AI applied to ultrasound imaging in benign gynecological disorders.</p><p><strong>Methods: </strong>Web of Science, PubMed and Scopus databases were searched from inception until August 2024. Inclusion criteria were studies applying AI to ultrasound imaging in the diagnosis and management of benign gynecological disorders. Studies retrieved from the literature search were imported into Rayyan software and quality assessment was performed using the Quality Assessment Tool for Artificial Intelligence-Centered Diagnostic Test Accuracy Studies (QUADAS-AI).</p><p><strong>Results: </strong>Of the 59 studies included, 12 were on polycystic ovary syndrome (PCOS), 11 were on infertility and assisted reproductive technology, 11 were on benign ovarian pathology (i.e. ovarian cysts, ovarian torsion, premature ovarian failure), 10 were on endometrial or myometrial pathology, nine were on pelvic floor disorder and six were on endometriosis. China was the most highly represented country (22/59 (37.3%)). According to QUADAS-AI, most studies were at high risk of bias for the subject selection domain (because the sample size, source or scanner model was not specified, data were not derived from open-source datasets and/or imaging preprocessing was not performed) and the index test domain (AI models were not validated externally), and at low risk of bias for the reference standard domain (the reference standard classified the target condition correctly) and the workflow domain (the time between the index test and the reference standard was reasonable). Most studies (40/59) developed and internally validated AI classification models for distinguishing between normal and pathological cases (i.e. presence vs absence of PCOS, pelvic endometriosis, urinary incontinence, ovarian cyst or ovarian torsion), whereas 19/59 studies aimed to automatically segment or measure ovarian follicles, ovarian volume, endometrial thickness, uterine fibroids or pelvic floor structures.</p><p><strong>Conclusion: </strong>The published literature on AI applied to ultrasound in benign gynecological disorders is focused mainly on creating classification models to distinguish between normal and pathological cases, and on developing models to automatically segment or measure ovarian volume or follicles. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.</p>","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"295-302"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply.","authors":"K Mikolaj, C A Taksøe-Vester, O B B Petersen, N G Vejlstrup, A N Christensen, A Feragen, M Nielsen, M B S Svendsen, M G Tolsgaard","doi":"10.1002/uog.29160","DOIUrl":"10.1002/uog.29160","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"392-393"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thoughts on the contribution of artificial intelligence (AI) to assessment of the fetal heart: a true scientific odyssey.","authors":"E Quarello, E Corno","doi":"10.1002/uog.29185","DOIUrl":"10.1002/uog.29185","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"292-294"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of maternal body mass index with hemodynamic and vascular alterations at 35-37 weeks' gestation.","authors":"M Charakida, C Chatzakis, L A Magee, A Syngelaki, T Mansukhani, P von Dadelszen, K H Nicolaides","doi":"10.1002/uog.29170","DOIUrl":"10.1002/uog.29170","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Globally, one in four pregnant women is classified as overweight or obese, based on their prepregnancy body mass index (BMI). Obese pregnant women are at increased risk of adverse pregnancy outcomes and long-term cardiovascular disease that occurs earlier in life. This study aimed to assess maternal hemodynamic and vascular parameters at 35-37 weeks' gestation, to understand the alterations that may occur in association with increased maternal BMI and gestational weight gain, and to evaluate obesity-related pregnancy outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a prospective observational study of 11 731 women with a singleton pregnancy attending for a routine hospital visit at 35 + 0 to 36 + 6 weeks' gestation at King's College Hospital, London, UK, between December 2021 and June 2024. Women were categorized based on their BMI at 11-13 weeks' gestation, as normal weight (BMI, 18.5-24.9 kg/m&lt;sup&gt;2&lt;/sup&gt;), overweight (BMI, 25.0-29.9 kg/m&lt;sup&gt;2&lt;/sup&gt;) or obese (BMI, ≥ 30 kg/m&lt;sup&gt;2&lt;/sup&gt;). We recorded details regarding maternal demographic characteristics and medical history, used Doppler ultrasound to assess the uterine artery pulsatility index (UtA-PI) (as a marker for uteroplacental perfusion) and ophthalmic artery peak systolic velocity (PSV) ratio (as a marker for small vessel peripheral circulation), and measured carotid-to-femoral pulse-wave velocity, augmentation index (as direct and indirect markers of aortic stiffness, respectively), cardiac output, total peripheral resistance (TPR), and central systolic and diastolic blood pressure. Multivariable analysis was performed to examine the relationship of BMI and gestational weight gain with hemodynamic and vascular measures, adjusting for maternal demographics, medical history, pregnancy characteristics and pregnancy outcomes (including pre-eclampsia and gestational diabetes mellitus).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overweight and obese women were more often of black ethnicity, and had higher central systolic and diastolic blood pressure, cardiac output, aortic stiffness and UtA-PI, compared with normal-weight women. There was no significant difference between overweight or obese women and normal-weight women with regard to TPR and ophthalmic artery PSV ratio. On multivariable analysis, increasing BMI at 11-13 weeks and gestational weight gain between 11-13 weeks and 35-37 weeks were independently associated with increases in all cardiovascular indices (including ophthalmic artery PSV ratio), apart from TPR.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Women with a high BMI in early pregnancy vs normal-weight women, and those with higher gestational weight gain, had worse maternal hemodynamic and vascular indices at 35-37 weeks' gestation, independent of baseline and pregnancy characteristics. Our findings support the notion that optimization of prepregnancy weight and gestational weight gain may improve maternal hemodynamics and vascular function during pregnancy","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"303-310"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Could maternal rest improve adverse outcome in fetuses defined by abnormal growth trajectory?","authors":"G R DeVore","doi":"10.1002/uog.29152","DOIUrl":"10.1002/uog.29152","url":null,"abstract":"","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"393-394"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound.","authors":"F Moro, M Vagni, H E Tran, F Bernardini, F Mascilini, F Ciccarone, C Nero, D Giannarelli, L Boldrini, A Fagotti, G Scambia, L Valentin, A C Testa","doi":"10.1002/uog.27680","DOIUrl":"10.1002/uog.27680","url":null,"abstract":"<p><strong>Objective: </strong>The primary aim was to identify radiomics ultrasound features that can distinguish between benign and malignant adnexal masses with solid ultrasound morphology, and between primary malignant (including borderline and primary invasive) and metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. The secondary aim was to compare the discrimination performance of our newly developed radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and that of subjective assessment by an experienced ultrasound examiner.</p><p><strong>Methods: </strong>This was a retrospective, observational single-center study conducted at Fondazione Policlinico Universitario A. Gemelli IRCC, in Rome, Italy. Included were patients with a histological diagnosis of an adnexal tumor with solid morphology according to International Ovarian Tumor Analysis (IOTA) terminology at preoperative ultrasound examination performed in 2014-2020, who were managed with surgery. The patient cohort was split randomly into training and validation sets at a ratio of 70:30 and with the same proportion of benign and malignant tumors in the two subsets, with malignant tumors including borderline, primary invasive and metastatic tumors. We extracted 68 radiomics features, belonging to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model-building were patient age and radiomics features that were statistically significantly different between benign and malignant adnexal masses and assessed as not redundant based on the Pearson correlation coefficient. We evaluated the discriminative ability of the models and compared it to that of the ADNEX model and that of subjective assessment by an experienced ultrasound examiner using the area under the receiver-operating-characteristics curve (AUC) and classification performance by calculating sensitivity and specificity.</p><p><strong>Results: </strong>In total, 326 patients were included and 775 preoperative ultrasound images were analyzed. Of the 68 radiomics features extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 uncorrelated features were selected for inclusion in model-building. The same 52 radiomics features differed significantly between benign, primary malignant and metastatic tumors. However, the values of the features manifested overlapped between primary malignant and metastatic tumors and did not differ significantly between them. In the validation set, 25/98 (25.5%) tumors were benign and 73/98 (74.5%) were malignant (6 borderline, 57 primary inv","PeriodicalId":23454,"journal":{"name":"Ultrasound in Obstetrics & Gynecology","volume":" ","pages":"353-363"},"PeriodicalIF":6.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}