{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T07:49:42Z","timestamp":1791532182318,"version":"4.3.4"},"reference-count":13,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100006775","name":"GE Healthcare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pregnancy"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cardiotocography (CTG) is widely used for monitoring fetal heart rate (FHR) and uterine activity (UA) during pregnancy and labor. Clinician interpretation of CTG tracings may be subjective, leading to significant inter\u2010 and intra\u2010observer variability and inconsistent clinical decision\u2010making. An artificial intelligence (AI) algorithm using deep learning and rule\u2010based techniques was developed to label FHR and UA data during labor. Further development of such algorithms may add future objective monitor outputs to support clinician interpretation. This study assessed the non\u2010inferiority of the algorithm's performance compared to registered nurse (RN) reader assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective non\u2010inferiority assessment utilized CTG data across five US hospital facilities (2013\u20132024). CTG labeling assessment by the AI algorithm was compared with that of RN readers for primary endpoints (baseline value, accelerations, decelerations, contraction frequency, and contraction duration) and secondary endpoints (variability and deceleration types), using a panel of qualified expert RNs who accepted or rejected the assessment. Exploratory endpoints included frequency of true and false positives\/negatives and sensitivity\/specificity of accelerations and decelerations. A non\u2010inferiority threshold of 15% was required between the RN reader and algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 2639 de\u2010identified fetal tracings, 800 were randomly selected for this study. The most frequently reported maternal characteristics were full\u2010term gestational age (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0355; 44.38%) and non\u2010Hispanic White (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0409; 51.13%). The algorithm performed comparably to the RN reader for all endpoints except contraction duration, with percent differences (95% confidence interval [CI]) in acceptance rates (ARs) of 3.38% (\u22121.5%, 8.25%) for baseline value, 3.03% (0.72%, 5.34%) for acceleration, 7.14% (3.68%, 10.61%) for deceleration, 8.25% (3.57%, 12.93%) for contraction frequency, 22.13% (17.39%, 26.86%) for contraction duration, \u22121.75% (\u22124.10%, 0.60%) for variability, and 9.13% (5.38%, 12.88%) for deceleration type. For contraction duration, AR decreased to 13.75% (9.89%, 17.61%) when allowing for a 20\u2010s range of variability to account for visual approximations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrated the non\u2010inferiority of a novel AI algorithm compared to RN readers in analyzing and assessing fetal tracings. These findings highlight AI's potential to serve as a decision\u2010making support tool in obstetric care. By providing consistent analyses, the algorithm may help reduce observer variability and enhance clinicians\u2019 confidence in identifying fetal parameters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pmf2.70433","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:02:56Z","timestamp":1787839376000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non\u2010inferiority analysis of a fetal heart rate artificial intelligence algorithm to registered nurse assessment"],"prefix":"10.1002","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2444-1025","authenticated-orcid":false,"given":"Rohit","family":"Pardasani","sequence":"first","affiliation":[{"name":"Patient Care Solutions GE HealthCare Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2766-9822","authenticated-orcid":false,"given":"Renee","family":"Vitullo","sequence":"additional","affiliation":[{"name":"Patient Care Solutions GE HealthCare Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Harris","sequence":"additional","affiliation":[{"name":"Patient Care Solutions GE HealthCare Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3153-9546","authenticated-orcid":false,"given":"Karen P.","family":"Becker","sequence":"additional","affiliation":[{"name":"Patient Care Solutions GE HealthCare Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Halit O.","family":"Yapici","sequence":"additional","affiliation":[{"name":"Boston Strategic Partners Inc  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4719-3893","authenticated-orcid":false,"given":"John W.","family":"Beard","sequence":"additional","affiliation":[{"name":"Patient Care Solutions GE HealthCare Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010024\u201063108\u20106"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3892\/mi.2024.151"},{"key":"e_1_2_10_4_1","volume-title":"NICHD Definitions and Classifications: Application to Electronic Fetal Monitoring Interpretation","author":"Simpson K. 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