AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation

Ultrasound Clinical Trial

— scan-AId  

Official title:

Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06232187
• Other study ID #: F-24001576
• Status: Enrolling by invitation
• Phase: N/A
• Start date: February 14, 2024
• Est. completion date: September 1, 2024

Study information

• Verified date: April 2024
• Source: Copenhagen Academy for Medical Education and Simulation
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI.

Description:

The goal of this randomized controlled clinical trial is to learn which type of artificial intelligence (AI) effects the diagnostic accuracy of ultrasound estimation of fetal weight (EFW), when performed by novices, in this study represented by medical students. The study's objectives are: - Which type of artificial intelligence support system works for novices in improving the ultrasound fetal weight diagnostic accuracy? - Does the artificial intelligence improve image quality, evaluate the cognitive load placed on participants when utilizing AI support, and is the AI system usable for novices? Participants will be tasked with conducting an ultrasound Estimated Fetal Weight (EFW) using either a simple black box AI or a detailed explainable AI feedback system. The AI systems will assist participants in determining if they have captured the appropriate image for EFW. The outcomes will then be compared to those of a control group. Ultrasound procedures will be performed on pregnant women with fetuses at a gestational age of 28-42 weeks, who have previously undergone an EFW by an expert sonographer or doctor at the clinic within 5 days days leading up to the examinationday. One participant of each randomization arm, will perfrom an EFW on the same pregnant woman.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 75
• Est. completion date: September 1, 2024
• Est. primary completion date: June 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Ultrasound novice participants:

Inclusion Criteria:

• Medical students with no former fetal or abdominal ultrasound training.
• The participants will have to understand spoken and written Danish or English.

Exclusion Criteria:

• Medical students who received formal fetal or abdominal training prior to the inclusion in this study. Pregnant women;

Inclusion Criteria:

• The participants will have to understand spoken and written Danish or English.
• BMI < 30
• Gestational age: 28-42

Exclusion Criteria:

• Age > 40 years
• Fefal anomaly
• Oligohydramnion
• Gestational Diabetes, Diabetes type 1 or 2.

Related Conditions & MeSH terms

• Body Weight
• Fetal Weight
• Ultrasound

Intervention

Behavioral:
• Artificial Intelligence feedback for ultrasound EFW standard plane images
AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation.

Locations

Country	Name	City	State
Denmark	Rigshospitalet	Copenhagen

Sponsors (3)

Lead Sponsor	Collaborator
Copenhagen Academy for Medical Education and Simulation	Slagelse Hospital, Technical University of Denmark

Country where clinical trial is conducted

Denmark, 

References & Publications (16)

Andreasen LA, Feragen A, Christensen AN, Thybo JK, Svendsen MBS, Zepf K, Lekadir K, Tolsgaard MG. Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization. Sci Rep. 2023 Feb 8;13(1):2221. doi: 10.1038/s41598-023-29105-x. — View Citation

Andreasen LA, Tabor A, Norgaard LN, Rode L, Gerds TA, Tolsgaard MG. Detection of growth-restricted fetuses during pregnancy is associated with fewer intrauterine deaths but increased adverse childhood outcomes: an observational study. BJOG. 2021 Jan;128(1):77-85. doi: 10.1111/1471-0528.16380. Epub 2020 Jul 27. — View Citation

Andreasen LA, Tabor A, Norgaard LN, Taksoe-Vester CA, Krebs L, Jorgensen FS, Jepsen IE, Sharif H, Zingenberg H, Rosthoj S, Sorensen AL, Tolsgaard MG. Why we succeed and fail in detecting fetal growth restriction: A population-based study. Acta Obstet Gynecol Scand. 2021 May;100(5):893-899. doi: 10.1111/aogs.14048. Epub 2021 Jan 12. — View Citation

Bloch R, Norman G. Generalizability theory for the perplexed: a practical introduction and guide: AMEE Guide No. 68. Med Teach. 2012;34(11):960-92. doi: 10.3109/0142159X.2012.703791. — View Citation

Borsci S, Federici S, Lauriola M. On the dimensionality of the System Usability Scale: a test of alternative measurement models. Cogn Process. 2009 Aug;10(3):193-7. doi: 10.1007/s10339-009-0268-9. Epub 2009 Jun 30. — View Citation

Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e549-e560. doi: 10.1016/S2589-7500(20)30219-3. Epub 2020 Sep 9. — View Citation

Degallier-Rochat S, Kurpicz-Briki M, Endrissat N, Yatsenko O. Human augmentation, not replacement: A research agenda for AI and robotics in the industry. Front Robot AI. 2022 Oct 4;9:997386. doi: 10.3389/frobt.2022.997386. eCollection 2022. No abstract available. — View Citation

Govaerts MJ, Schuwirth LW, Van der Vleuten CP, Muijtjens AM. Workplace-based assessment: effects of rater expertise. Adv Health Sci Educ Theory Pract. 2011 May;16(2):151-65. doi: 10.1007/s10459-010-9250-7. Epub 2010 Sep 30. — View Citation

Hadlock FP. Sonographic estimation of fetal age and weight. Radiol Clin North Am. 1990 Jan;28(1):39-50. — View Citation

Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e537-e548. doi: 10.1016/S2589-7500(20)30218-1. Epub 2020 Sep 9. — View Citation

Nicholls D, Sweet L, Hyett J. Psychomotor skills in medical ultrasound imaging: an analysis of the core skill set. J Ultrasound Med. 2014 Aug;33(8):1349-52. doi: 10.7863/ultra.33.8.1349. — View Citation

Salomon LJ, Alfirevic Z, Da Silva Costa F, Deter RL, Figueras F, Ghi T, Glanc P, Khalil A, Lee W, Napolitano R, Papageorghiou A, Sotiriadis A, Stirnemann J, Toi A, Yeo G. ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol. 2019 Jun;53(6):715-723. doi: 10.1002/uog.20272. — View Citation

Tolsgaard MG, Boscardin CK, Park YS, Cuddy MM, Sebok-Syer SS. The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. Adv Health Sci Educ Theory Pract. 2020 Dec;25(5):1057-1086. doi: 10.1007/s10459-020-10009-8. Epub 2020 Nov 3. — View Citation

Tolsgaard MG, Pusic MV, Sebok-Syer SS, Gin B, Svendsen MB, Syer MD, Brydges R, Cuddy MM, Boscardin CK. The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156. Med Teach. 2023 Jun;45(6):565-573. doi: 10.1080/0142159X.2023.2180340. Epub 2023 Mar 2. — View Citation

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7. — View Citation

Vasey B, Novak A, Ather S, Ibrahim M, McCulloch P. DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology. Clin Radiol. 2023 Feb;78(2):130-136. doi: 10.1016/j.crad.2022.09.131. — View Citation

* Note: There are 16 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	The AI system usability	The participants will be asked to answer a questionnaire: System Usability Scale (SUS), which is used to evaluate the AI feedback system.; Min 1 Maximum 100. A higher score indicating better system usability.	5 minutes
Other	Measurement of the reaction time	Measurements of the participants reaction time will a measurement for the cognitive load.; The reaction time will be measured as a secondary task while the participants are performing the ultrasound scan.	5 minutes
Primary	Diagnostic accuracy	The accuracy in each group was defined as the percentage difference between estimated fetal weight and the sonographer expert EFW	15 minutes
Secondary	Image Quality	Salomon criteria score is used to rate the image quality. Points are given depending on the number of landmarks present, quality of the image optimization and caliper.placements.; Minimum: 1 Maximum: 18. A higher score indicates a better image quality.	5 minutes pr. participant