Artificial intelligence for the detection of fetal ultrasound findings concerning major congenital heart defects. A systematic review.
DOI:
https://doi.org/10.51168/sjhrafrica.v5i12.2415Keywords:
Artificial intelligence, Congenital heart defects, Deep learning, Fetal echocardiography, Fetal ultrasound, Prenatal screeningAbstract
Background: Prenatal detection of major congenital heart defects (CHDs) remains suboptimal despite routine fetal ultrasound screening, largely due to operator dependence, variability in expertise, and subtle morphologic presentations. Artificial intelligence (AI) has emerged as a potential adjunct to improve screening performance by identifying ultrasound findings concerning major CHDs.
Objective: To systematically review and synthesize current evidence on the role of artificial intelligence in detecting fetal ultrasound findings concerning major congenital heart defects.
Methods: A systematic literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar in accordance with PRISMA 2020 guidelines. Original peer-reviewed studies evaluating AI-based models applied to fetal ultrasound or fetal echocardiography for the detection of major CHDs or suspicious findings were included. Data were synthesized qualitatively, and the risk of bias was assessed using an adapted QUADAS-2 framework.
Results: This systematic review was not prospectively registered. Five studies published between 2021 and 2026 met the inclusion criteria. AI models demonstrated consistently high diagnostic performance, with reported sensitivities ranging from approximately 84% to 96.8% and specificity generally exceeding 90%. Models focusing on the detection of concerning ultrasound findings rather than lesion-specific diagnosis demonstrated particularly high screening sensitivity. Overall risk of bias was low to moderate.
Conclusion: Artificial intelligence showed strong potential as a screening adjunct for improving prenatal detection of major congenital heart defects, warranting further prospective validation in real-world clinical settings.
Implications and future research (addition):“These findings support the potential role of artificial intelligence–assisted fetal echocardiography as an adjunct to conventional ultrasound screening for major congenital heart defects. However, heterogeneity in study design, AI architectures, outcome reporting, and validation strategies limits direct clinical translation. Future research should prioritize large-scale prospective studies, external validation across diverse populations, standardized performance metrics, and evaluation of real-world clinical integration, including cost-effectiveness and ethical considerations.
References
Xu J, Li Q, Deng L, Xiong J, Cheng Z, Ye C. Global, regional and national epidemiology of congenital heart disease in children from 1990 to 2021. Front Cardiovasc Med. 2025 May 16;12:1522644. doi: 10.3389/fcvm.2025.1522644. PMID: 40454242; PMCID: PMC12122482.
Ayiga Majid, Johnes Obungoloch, Alfred Enywaku, Obeti Francis, Denis Jjuuko, Eugene Bizimana, Biryomumeisho Joshua, Wasswa William, Diagnosis of congenital heart diseases in children from 2D and 3D sonography using convolutional neural networks: A scoping literature review, WFUMB Ultrasound Open, Volume 3, Issue 2, 2025, 100096, ISSN 2949-6683, https://doi.org/10.1016/j.wfumbo.2025.100096.
Van Der Linde D, Konings EE, Slager MA, Witsenburg M, Helbing WA, Takkenberg JJ, et al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol. 2011;58(21):2241-7.
Lahm H, Schön P, Doppler S, Dreßen M, Cleuziou J, Deutsch MA, Ewert P, Lange R, Krane M. Tetralogy of Fallot and Hypoplastic Left Heart Syndrome - Complex Clinical Phenotypes Meet Complex Genetic Networks. Curr Genomics. 2015 Jun;16(3):141-58. doi: 10.2174/1389202916666150303232520. PMID: 26069455; PMCID: PMC4460219.
Feinstein JA, Benson DW, Dubin AM, Cohen MS, Maxey DM, Mahle WT, Pahl E, Villafañe J, Bhatt AB, Peng LF, Johnson BA, Marsden AL, Daniels CJ, Rudd NA, Caldarone CA, Mussatto KA, Morales DL, Ivy DD, Gaynor JW, Tweddell JS, Deal BJ, Furck AK, Rosenthal GL, Ohye RG, Ghanayem NS, Cheatham JP, Tworetzky W, Martin GR. Hypoplastic left heart syndrome: current considerations and expectations. J Am Coll Cardiol. 2012 Jan 3;59(1 Suppl):S1-42. doi: 10.1016/j.jacc.2011.09.022. Erratum in: J Am Coll Cardiol. 2012 Jan 31;59(5):544. PMID: 22192720; PMCID: PMC6110391.
Ali N, Donofrio MT. Delivery room and early postnatal management of neonates with congenital heart disease. Prenat Diagn. 2024 Jul;44(8):915-924. doi: 10.1002/pd.6617. Epub 2024 Jun 10. PMID: 38858803.
Johnson BA, Ades A. Delivery room and early postnatal management of neonates who have prenatally diagnosed congenital heart disease. Clin Perinatol. 2005 Dec;32(4):921-46, ix. doi: 10.1016/j.clp.2005.09.014. PMID: 16325670.
Yun SW. Congenital heart disease in the newborn requires early intervention. Korean J Pediatr. 2011 May;54(5):183-91. doi: 10.3345/kjp.2011.54.5.183. Epub 2011 May 31. PMID: 21829408; PMCID: PMC3145901.
Menahem S, Sehgal A, Meagher S. Early detection of significant congenital heart disease: The contribution of fetal cardiac ultrasound and newborn pulse oximetry screening. J Paediatr Child Health. 2021 Mar;57(3):323-327. doi: 10.1111/jpc.15355. Epub 2021 Feb 2. PMID: 33529483.
Bonnet D. Impacts of prenatal diagnosis of congenital heart diseases on outcomes. Transl Pediatr. 2021 Aug;10(8):2241-2249. doi: 10.21037/tp-20-267. PMID: 34584895; PMCID: PMC8429871.
Patel SR, Michelfelder E. Prenatal Diagnosis of Congenital Heart Disease: The Crucial Role of Perinatal and Delivery Planning. J Cardiovasc Dev Dis. 2024 Mar 31;11(4):108. doi: 10.3390/jcdd11040108. PMID: 38667726; PMCID: PMC11050606.
Johnson BA, Shepherd J, Bhombal S, Ali N, Joynt C. Special considerations for the stabilization and resuscitation of patients with cardiac disease in the Neonatal Intensive Care Unit. Semin Perinatol. 2024 Dec;48(8):151989. doi: 10.1016/j.semperi.2024.151989. Epub 2024 Oct 8. PMID: 39477714.
Popa AI, Cernea N, Marinaș MC, Comănescu MC, Sîrbu OC, Popa DG, Pătru L, Pădureanu V, Pătru CL. Ultrasound Screening in the First and Second Trimester of Pregnancy for the Detection of Fetal Cardiac Anomalies in a Low-Risk Population. Diagnostics (Basel). 2025 Mar 19;15(6):769. doi: 10.3390/diagnostics15060769. PMID: 40150110; PMCID: PMC11941630.
Matthew J, Skelton E, Day TG, Zimmer VA, Gomez A, Wheeler G, Toussaint N, Liu T, Budd S, Lloyd K, Wright R, Deng S, Ghavami N, Sinclair M, Meng Q, Kainz B, Schnabel JA, Rueckert D, Razavi R, Simpson J, Hajnal J. Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time. Prenat Diagn. 2022 Jan;42(1):49-59. doi: 10.1002/pd.6059. Epub 2021 Oct 18. PMID: 34648206.
Moon-Grady AJ, Donofrio MT, Gelehrter S, Hornberger L, Kreeger J, Lee W, Michelfelder E, Morris SA, Peyvandi S, Pinto NM, Pruetz J, Sethi N, Simpson J, Srivastava S, Tian Z. Guidelines and Recommendations for Performance of the Fetal Echocardiogram: An Update from the American Society of Echocardiography. J Am Soc Echocardiogr. 2023 Jul;36(7):679-723. doi: 10.1016/j.echo.2023.04.014. Epub 2023 May 24. PMID: 37227365.
Ricketts, Robert M. et al. Commentary on the 2023 Guidelines and Recommendations for Performance of the Fetal Echocardiogram: An Update From the American Society of Echocardiography, Journal of Cardiothoracic and Vascular Anesthesia, Volume 37, Issue 12, 2431 - 2434
Bakker MK, Bergman JEH, Krikov S, Amar E, Cocchi G, Cragan J, de Walle HEK, Gatt M, Groisman B, Liu S, Nembhard WN, Pierini A, Rissmann A, Chidambarathanu S, Sipek A Jr, Szabova E, Tagliabue G, Tucker D, Mastroiacovo P, Botto LD. Prenatal diagnosis and prevalence of critical congenital heart defects: an international retrospective cohort study. BMJ Open. 2019 Jul 2;9(7):e028139. doi: 10.1136/bmjopen-2018-028139. PMID: 31270117; PMCID: PMC6609145.
Eckersley L, Lad M, Rose H, Lougheed J, Poolsaar H, Szabo A, Nield L, Fruitman D, Mansuskani G, Hornberger LK, Arya B, Dover D, Blagdon E, Freud L. Rate of prenatal diagnosis of critical congenital heart disease continued to improve despite COVID-19 pandemic: multicenter Canadian study. Ultrasound Obstet Gynecol. 2025 Nov;66(5):606-613. doi: 10.1002/uog.70028. Epub 2025 Sep 20. PMID: 40975049; PMCID: PMC12579774.
Davtyan A, Ostler H, Golding IF, Sun HY. Prenatal Diagnosis Rate of Critical Congenital Heart Disease Remains Inadequate with Significant Racial/Ethnic and Socioeconomic Disparities and Technical Barriers. Pediatr Cardiol. 2024 Dec;45(8):1713-1723. doi: 10.1007/s00246-023-03262-2. Epub 2023 Aug 30. PMID: 37648785; PMCID: PMC11442540.
Hashiramoto S, Kaneko M, Takita H, Yamashita Y, Matsuoka R, Sekizawa A. Factors affecting the accuracy of fetal cardiac ultrasound screening in the first trimester of pregnancy. J Med Ultrason (2001). 2025 Jan;52(1):131-138. doi: 10.1007/s10396-024-01505-0. Epub 2024 Nov 1. Erratum in: J Med Ultrason (2001). 2025 Jul;52(3):359. doi: 10.1007/s10396-025-01542-3. PMID: 39485582; PMCID: PMC12000114.
Herling L, Johnson J, Ferm-Widlund K, Bergholm F, Lindgren P, Sonesson SE, Acharya G, Westgren M. Automated analysis of fetal cardiac function using color tissue Doppler imaging. Ultrasound Obstet Gynecol. 2018 Nov;52(5):599-608. doi: 10.1002/uog.18812. Epub 2018 Oct 1. PMID: 28715153.
Weerakkody Y, Kang O, Goel A, et al. Four-chamber cardiac view (fetal). Reference article, Radiopaedia.org (Accessed on 26 Jan 2026) https://doi.org/10.53347/rID-15708.
Fontanella F, Bardi F, et al., Glob Libr Women's Med. ISSN: 1756-2228; DOI 10.3843/GLOWM.419353
Fernandez CO, Ramaciotti C, Martin LB, Twickler DM. The four-chamber view and its sensitivity in detecting congenital heart defects. Cardiology. 1998 Dec;90(3):202-6. doi: 10.1159/000006844. PMID: 9892769.
Pavlicek J, Klaskova E, Kapralova S, Palatova AM, Piegzova A, Spacek R, Gruszka T. Major heart defects: the diagnostic evaluations of first-year-olds. BMC Pediatr. 2021 Nov 30;21(1):528. doi: 10.1186/s12887-021-02997-2. PMID: 34847867; PMCID: PMC8630885.
Yun SW. Congenital heart disease in the newborn requires early intervention. Korean J Pediatr. 2011 May;54(5):183-91. doi: 10.3345/kjp.2011.54.5.183. Epub 2011 May 31. PMID: 21829408; PMCID: PMC3145901.
Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel). 2023 Dec 18;10(12):1435. doi: 10.3390/bioengineering10121435. PMID: 38136026; PMCID: PMC10740686.
D'Alberti E, Patey O, Smith C, Šalović B, Hernandez-Cruz N, Noble JA, Papageorghiou AT. Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis. EClinicalMedicine. 2025 May 30;84:103250. doi: 10.1016/j.eclinm.2025.103250. PMID: 40687738; PMCID: PMC12273734.
Suha KT, Lubenow H, Soria-Zurita S, Haw M, Vettukattil J, Jiang J. The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review. Medicina. 2025; 61(4):561. https://doi.org/10.3390/medicina61040561
Jassim, Shabana, Basheer, Shaheen. AI vs. Traditional ultrasound study in Congenital Heart Defect Detection: A Systematic review. 2025 medRxiv. 2025.04.05.25325217. 10.1101/2025.04.05.25325217
Liastuti LD, Nursakina Y. Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review and meta-analysis. Front Cardiovasc Med. 2025 Feb 24;12:1473544. doi: 10.3389/fcvm.2025.1473544. PMID: 40066351; PMCID: PMC11891181.
Buijtendijk M, Shah H, Lugthart MA, Dawood Y, Limpens J, Bakker BS, den Hoff MJB, Leeflang MMG, Pajkrt E. Diagnostic accuracy of ultrasound screening for fetal structural abnormalities during the first and second trimester of pregnancy in low‐risk and unselected populations. Cochrane Database Syst Rev. 2021 Jul 21;2021(7): CD014715. doi: 10.1002/14651858.CD014715. PMCID: PMC8406822.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924.
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011 Oct 18;155(8):529-36. doi: 10.7326/0003-4819-155-8-201110180-00009. PMID: 22007046.
Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med. 2021 May;27(5):882-891. doi: 10.1038/s41591-021-01342-5. Epub 2021 May 14. PMID: 33990806; PMCID: PMC8380434.
Tang J, Liang Y, Jiang Y, Liu J, Zhang R, Huang D, Pang C, Huang C, Luo D, Zhou X, Li R, Zhang K, Xie B, Hu L, Zhu F, Xia H, Lu L, Wang H. A multicenter study on a two-stage transfer learning model for duct-dependent CHD screening in fetal echocardiography. NPJ Digit Med. 2023 Aug 12;6(1):143. doi: 10.1038/s41746-023-00883-y. PMID: 37573426; PMCID: PMC10423245.
Athalye C, van Nisselrooij A, Rizvi S, Haak MC, Moon-Grady AJ, Arnaout R. Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection. Ultrasound Obstet Gynecol. 2024 Jan;63(1):44-52. doi: 10.1002/uog.27503. PMID: 37774040; PMCID: PMC10841849.
Lei W, Wen C, Li H, Yang S, Shen K, Yuan H, Li H, Xu H, Gao X, Zhang S, Yang Z, Ye M, Du B, Wu Q. An interpretable deep learning model for first-trimester fetal cardiac screening. NPJ Digit Med. 2025 Dec 8;9(1):43. doi: 10.1038/s41746-025-02217-6. PMID: 41361059; PMCID: PMC12800213.
Zelop CM, Lam-Rachlin J, Arunamata A, Punn R, Behera SK, Lachaud M, David N, DeVore GR, Rebarber A, Fox NS, Gayanilo M, Garmel S, Boukobza P, Uzan P, Joly H, Girardot R, Cohen L, Stos B, De Boisredon M, Askinazi E, Thorey V, Gardella C, Levy M, Geiger M. Artificial Intelligence for the Detection of Fetal Ultrasound Findings Concerning Major Congenital Heart Defects. Obstet Gynecol. 2026 Jan 1;147(1):97-107. doi: 10.1097/AOG.0000000000006027. Epub 2025 Aug 7. PMID: 40773751; PMCID: PMC12704680.
Sharland GK, Allan LD. Screening for congenital heart disease prenatally. Results of a 2 1/2-year study in the South East Thames Region. Br J Obstet Gynaecol. 1992 Mar;99(3):220-5. doi: 10.1111/j.1471-0528.1992.tb14503.x. PMID: 1606121.
Wren C, Reinhardt Z, Khawaja K. Twenty-year trends in diagnosis of life-threatening neonatal cardiovascular malformations. Arch Dis Child Fetal Neonatal Ed. 2008 Jan;93(1):F33-5. doi: 10.1136 /adc.2007.119032. Epub 2007 Jun 7. PMID: 17556383.
Garne E, Stoll C, Clementi M; Euroscan Group. Evaluation of prenatal diagnosis of congenital heart diseases by ultrasound: experience from 20 European registries. Ultrasound Obstet Gynecol. 2001 May;17(5):386-91. doi: 10.1046/j.1469-0705.2001.00385.x. PMID: 11380961.
Tegnander E, Williams W, Johansen OJ, Blaas HG, Eik-Nes SH. Prenatal detection of heart defects in a non-selected population of 30,149 fetuses--detection rates and outcome. Ultrasound Obstet Gynecol. 2006 Mar;27(3):252-65. doi: 10.1002/uog.2710. PMID: 16456842.
Carvalho JS, Mavrides E, Shinebourne EA, Campbell S, Thilaganathan B. Improving the effectiveness of routine prenatal screening for major congenital heart defects. Heart. 2002 Oct;88(4):387-91. doi: 10.1136/heart.88.4.387. PMID: 12231598; PMCID: PMC1767391.
Yagel S, Cohen SM, Achiron R. Examination of the fetal heart by five short-axis views: a proposed screening method for comprehensive cardiac evaluation. Ultrasound Obstet Gynecol. 2001 May;17(5):367-9. doi: 10.1046/j.1469-0705.2001.00414.x. PMID: 11380958.
Bravo-Valenzuela NJ, Peixoto AB, Araujo Júnior E. Prenatal diagnosis of congenital heart disease: A review of current knowledge. Indian Heart J. 2018 Jan-Feb;70(1):150-164. doi: 10.1016/j.ihj.2017.12.005. Epub 2017 Dec 16. PMID: 29455772; PMCID: PMC5903017.
Sun HY. Prenatal diagnosis of congenital heart defects: echocardiography. Transl Pediatr. 2021 Aug;10(8):2210-2224. doi: 10.21037/tp-20-164. PMID: 34584892; PMCID: PMC8429868.
Carvalho JS, Axt-Fliedner R, Chaoui R, Copel JA, Cuneo BF, Goff D, Gordin Kopylov L, Hecher K, Lee W, Moon-Grady AJ, Mousa HA, Munoz H, Paladini D, Prefumo F, Quarello E, Rychik J, Tutschek B, Wiechec M, Yagel S. ISUOG Practice Guidelines (updated): fetal cardiac screening. Ultrasound Obstet Gynecol 2023; 61: 788–803.
Rizzo G, Arduini D, Romanini C. Doppler echocardiographic assessment of fetal cardiac function. Ultrasound Obstet Gynecol. 1992 Nov 1;2(6):434-45. doi: 10.1046/j.1469-0705.1992.02060434.x. PMID: 12796921.
Huggon IC, Ghi T, Cook AC, Zosmer N, Allan LD, Nicolaides KH. Fetal cardiac abnormalities are identified before 14 weeks of gestation. Ultrasound Obstet Gynecol. 2002 Jul;20(1):22-9. doi: 10.1046/j.1469-0705.2002.00733.x. PMID: 12100413.
Smrcek JM, Berg C, Geipel A, Fimmers R, Axt-Fliedner R, Diedrich K, Gembruch U. Detection rate of early fetal echocardiography and in utero development of congenital heart defects. J Ultrasound Med. 2006 Feb;25(2):187-96. doi: 10.7863/jum.2006.25.2.187. PMID: 16439781.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26. PMID: 28778026.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019 Jan;25(1):24-29. doi: 10.1038/s41591-018-0316-z. Epub 2019 Jan 7. PMID: 30617335.
Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert D. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound. IEEE Trans Med Imaging. 2017 Nov;36(11):2204-2215. doi: 10.1109/TMI.2017.2712367. Epub 2017 Jul 11. PMID: 28708546; PMCID: PMC6051487.
Chen H, Ni D, Qin J, Li S, Yang X, Wang T, Heng PA. Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks. IEEE J Biomed Health Inform. 2015 Sep;19(5):1627-36. doi: 10.1109/JBHI.2015.2425041. Epub 2015 Apr 21. PMID: 25910262.
Yan L, Ling S, Mao R, Xi H, Wang F. A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images. Biomed Eng Online. 2024 Apr 2;23(1):39. doi: 10.1186/s12938-024-01230-2. PMID: 38566181; PMCID: PMC10985891.
Komatsu M, Sakai A, Komatsu R, Matsuoka R, Yasutomi S, Shozu K, Dozen A, Machino H, Hidaka H, Arakaki T, et al. Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Applied Sciences. 2021; 11(1):371. https://doi.org/10.3390/app11010371
Dozen A, Komatsu M, Sakai A, Komatsu R, Shozu K, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Aoki D, Sekizawa A, Hamamoto R. Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules. 2020 Nov 8;10(11):1526. doi: 10.3390/biom10111526. PMID: 33171658; PMCID: PMC7695246.
Taksoee-Vester CA, Mikolaj K, Bashir Z, Christensen AN, Petersen OB, Sundberg K, Feragen A, Svendsen MBS, Nielsen M, Tolsgaard MG. AI-supported fetal echocardiography with quality assessment. Sci Rep. 2024 Mar 9;14(1):5809. doi: 10.1038/s41598-024-56476-6. PMID: 38461322; PMCID: PMC10925034.
He G. OC01.06: To evaluate the value of AI quality control in nine standard sections of the fetal heart. Ultrasound in Obstetrics & Gynecology. 2023 62(S1), 3-3. https://doi.org/10.1002/uog.26326
Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R, Kahn CE Jr, Esteva A, Karthikesalingam A, Mateen B, Webster D, Milea D, Ting D, Treanor D, Cushnan D, King D, McPherson D, Glocker B, Greaves F, Harling L, Ordish J, Cohen JF, Deeks J, Leeflang M, Diamond M, McInnes MDF, McCradden M, Abràmoff MD, Normahani P, Markar SR, Chang S, Liu X, Mallett S, Shetty S, Denniston A, Collins GS, Moher D, Whiting P, Bossuyt PM, Darzi A. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med. 2021 Oct;27(10):1663-1665. doi: 10.1038/s41591-021-01517-0. PMID: 34635854.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015 Jan 7;350:g7594. doi: 10.1136/bmj.g7594. PMID: 25569120.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr.Suvarna Palanivelu, Dr. Karthik Shunmugavelu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
