{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":13667132,"items":[{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:08:15Z","timestamp":1786975695318,"version":"build-2736575974"},"reference-count":15,"publisher":"American Medical Association (AMA)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Artificial intelligence (AI) systems have been developed to estimate gestational age (GA) based on blind sweep ultrasonography scans performed by novice operators. However, their generalizability to new clinical contexts outside original training settings requires evaluation.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To investigate the ability of a previously trained AI system to interpret blind sweep ultrasonography scans for GA estimation to generalize to new clinical environments.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This prospective diagnostic study compares AI model performance against the clinical standard for GA determination. The model was primarily accessed following lightweight adaptation using data from 1 setting. From a broader cohort of 2043 participants (1008 in Chicago and 1035 in Nairobi), datasets included an adaptation set of 120 Chicago participants (split evenly for training and validation) and a primary evaluation set of 385 participants (192 in Chicago and 193 in Nairobi) with GAs of 16 to 36 weeks. The study took place in 2 urban medical institutions, 1 in Chicago, Illinois (March 7, 2022, to May 28, 2024) and 1 in Nairobi, Kenya (January 8 to September 2, 2024). Data were analyzed April 21 to June 4, 2025.<\/jats:p>\n <\/jats:sec>\n \n Intervention<\/jats:title>\n A mobile application containing the AI system for GA estimation, with data collected via a protocolized blind sweep procedure by novice operators.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n The primary outcome was the mean absolute error (MAE) of the fine-tuned AI\u2019s GA estimation compared with the clinical standard (expert sonographer estimate), using a 1-day noninferiority margin.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The primary evaluation set included 385 participants (mean [SD] maternal age, 31.3 [5.6] years). The adapted model achieved an MAE of 4.1 days (95% CI, 3.5-4.7) in Chicago and 4.3 days (95% CI, 3.8-4.8 days) in Nairobi. Overall, the AI model demonstrated noninferiority to clinical standard estimates (MAE, 4.2 days [95% CI, 3.8-4.6 days] vs 4.5 days [95% CI, 4.1-4.9 days];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n These findings suggest that the AI model for GA estimation, using blind sweep data collected by novice operators, demonstrated generalization to new clinical settings and a different ultrasonography probe. This study highlighted the potential of AI-based ultrasonography assessment to expand access to care through scalable cross-setting deployment, particularly in low-resource settings.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.22484","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:00:31Z","timestamp":1783609231000},"page":"e2622484","source":"Crossref","is-referenced-by-count":1,"title":["Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography"],"prefix":"10.1001","volume":"9","author":[{"given":"Angelica","family":"Willis","sequence":"first","affiliation":[{"name":"Google, Mountain View, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chace","family":"Lee","sequence":"additional","affiliation":[{"name":"Google, Mountain View, 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Reproductive patterns in the domestic dog\u2014a retrospective study of the Drever breed. International M.Sc. Thesis. 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J. Biom."],"abstract":"The ultrasound parameters are very important factors for the estimation of gestational age (GS) decreases as pregnancy advances in age. Therefore, it is essential to reconnoiter other parameters that may complement the established fetal biometric parameters in predicting GS, especially in late pregnancy. The main objective of this study is to determine the relationship between GS and sonographic placental thickness (PL) as well as fetal parameters for the second (21-30 weeks) and third trimesters (31-40 weeks) using regression analysis. The mean scores for PL, GS, and weight of fetal were found 2.56\u00b10.38 cm, 165.15\u00b115.60 days and 654.31\u00b1266.68 gm respectively for 21-30 weeks. Similarly mean scores were found to correspond to\u00a0fetal parameters for 31-40 weeks.\u00a0 The relationship between GA (Y) in days and PT & BPD in cm. In the 21-30 weeks, \u00a0and GS 21-30 weeks = 146.36+7.70PL 21-30 weeks = 74.03+15.8BPD 21-30weeks. The relationship between GS and fetal parameters (BPD, AC, FL, etc.) are obtained as follows: GS 21-30 weeks=124.96+1.49BPD-0.34HC+0.54AC-0.61FL-1.2PL+0.05Weight. Gestational age has been estimated using two regression lines, first using three variables and second using all fetal parameters. It has been observed that Gestational age can be estimated accurately using all fetal parameters for both periods (21-30 & 31-40 weeks).<\/jats:p>","DOI":"10.28951\/bjb.v43i4.812","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T13:17:06Z","timestamp":1761139026000},"source":"Crossref","is-referenced-by-count":0,"title":["The evaluation of gestational age concerning placental thickness and fetal parameters using ultrasonography"],"prefix":"10.28951","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5064-5569","authenticated-orcid":false,"given":"Kamlesh Kumar","family":"Shukla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arvind","family":"Kumar 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A.","family":"Stringer","sequence":"additional","affiliation":[{"name":"UNC Global Projects\u2013Zambia, LLC, Lusaka, Zambia"},{"name":"Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"150","reference":[{"key":"e_1_3_5_2_2","unstructured":"World Health Organization. WHO Recommendations on Antenatal Care for a Positive Pregnancy Experience. 2016 (https:\/\/www.who.int\/reproductivehealth\/publications\/maternal_perinatal_health\/anc-positive-pregnancy-experience\/en\/)."},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1988.03410220090034"},{"key":"e_1_3_5_4_2","first-page":"294","article-title":"Statistical studies on menstruation: a criticism on the definition of normal menstruation","volume":"11","year":"1962","unstructured":"Matsumoto S, Nogami Y, Ohkuri S. Statistical studies on menstruation: a criticism on the definition of normal menstruation. Gunma J Med Sci 1962;11:294-318.","journal-title":"Gunma J Med Sci"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1968.03140060001001"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(21)00442-3"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194840"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/tmi.12657"},{"key":"e_1_3_5_9_2","volume-title":"JAMA guide to statistics and methods.","unstructured":"Carin L, Pencina MJ. On deep learning for medical image analysis. In: Livingston EH, Lewis RJ, eds. JAMA guide to statistics and methods. New York: McGraw-Hill Education, 2019;1192-1193."},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijgo.2010.11.013"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.12686"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.12688\/gatesopenres.12820.3"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.12688\/gatesopenres.13046.2"},{"key":"e_1_3_5_15_2","unstructured":"Zambian Ministry of Health. Annual Health Statistics Report 2017-2019. October 2020 (https:\/\/www.moh.gov.zm\/?wpfb_dl=159)."},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(19)30060-9"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1917130"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1799-6"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"}],"container-title":["NEJM Evidence"],"language":"en","deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:36:18Z","timestamp":1657715778000},"score":25.43203,"resource":{"primary":{"URL":"https:\/\/evidence.nejm.org\/doi\/10.1056\/EVIDoa2100058"}},"issued":{"date-parts":[[2022,4,26]]},"references-count":19,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,4,26]]}},"alternative-id":["10.1056\/EVIDoa2100058"],"URL":"https:\/\/doi.org\/10.1056\/evidoa2100058","relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.1056\/EVIDdo000003","asserted-by":"subject"},{"id-type":"doi","id":"10.1056\/EVIDdo000003","asserted-by":"subject"},{"id-type":"doi","id":"10.1056\/EVIDdo000003","asserted-by":"subject"}],"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.11.22.21265452","asserted-by":"object"}]},"ISSN":["2766-5526"],"issn-type":[{"value":"2766-5526","type":"print"}],"published":{"date-parts":[[2022,4,26]]}},{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:43:02Z","timestamp":1781739782063,"version":"3.54.5"},"reference-count":0,"publisher":"Nepal Journals Online (JOL)","issue":"2","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Nobel Med Coll"],"abstract":"Background: The correct estimation of gestational age is of great importance for managing pregnancy. Determining gestational age by the first-trimester scan is considered the gold standard, but classical calculation from the last menstrual period is also useful. Fetal biometry is widely used, but estimation from trans-cerebellar diameter can be very important, especially in low-income countries.\r\nMaterial and Methods: This was a prospective analytical study carried out in a Tertiary Care Centre from 21stMarch, 2023 to 20thMarch, 2024. Ethical clearance was taken from the Institutional Review Committee (Ref no:817\/2023). Consecutive sampling was done to include 384 pregnant women in the third trimester of pregnancy. Variables collected were demographics, last menstrual period, first-trimester scan, and obstetrical ultrasound including fetal biometry and trans-cerebellar diameter. Analytical analysis for correlation and coefficient of determination was done.\r\nResults: A total of 384 third-trimester pregnancies were studied. A statistically significant correlation was seen between gestational age from trans cerebellar diameter and first-trimester scan (r=0.965, R2=93.12%,p<0.01) and with last menstrual period (r=0.941, R2=88.54%,p<0.01). The correlation of the first-trimester scan was better with trans-cerebellar diameter than with fetal biometry (r=0.938, R2=87.98%, p<0.01). The estimation of gestational age from trans-cerebellar diameter was better in the early weeks of the third trimester than in the later weeks.\r\nConclusion: Trans-cerebellar diameter is an accurate parameter in the determination of gestational age in the third trimester. It is a better parameter to determine gestational age when compared to fetal biometry especially in the third trimester.<\/jats:p>","DOI":"10.3126\/jonmc.v13i2.74460","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T10:36:09Z","timestamp":1739874969000},"page":"55-59","source":"Crossref","is-referenced-by-count":2,"title":["Ultrasonography Guided Gestational Age Estimation Using Transcerebellar Diameter in Third Trimester 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Study Design: Cross-sectional descriptive study.Place and Duration of Study: Department of Obstetrics and Gynecology, Bahawal VictoriaHospital, Bahawalpur from Jun 2012 to Dec 2012. Methodology: This study was performed on327 patients in third trimester of pregnancy from 28-40 weeks fulfilling the inclusion criteria.Ultrasound measurements of transcerebellar diameter (TCD) and femur (FL) were made withcommercially available real time ultrasound equipment Toshiba Nemio-10 model 2009,Transducer frequency 50\/60 Hz. Collected data was analyzed by SPSS version 10. Results: Outof 327 patients, TCD was found to give correct assessment corresponding to the gestational ageby LMP in 262 (80.1%) patients, while in 232 (70.9%) patients FL was found to give correctassessment corresponding to the gestational age by LMP. Conclusions: Transcerebellardiameter is more reliable method of gestational age determination in third trimester of pregnancythan femur length. 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