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| Name | Class |
|---|---|
| Slagelse Hospital | OTHER |
| Technical University of Denmark | OTHER |
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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.
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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Feedback Group 1 (FG1) | Experimental | Participatns in FG1 will receive basic black box AI support, with simple explanation like "standard plane", "non standard plane" or "off plane". |
|
| Feedback Group 2 (FG2) | Experimental | Participants in FG2 will receive explainable AI support, with more elaborate description of the anatomical structures and segmentation of the anatomy. |
|
| Control group (CG) | No Intervention | Participants in the CG will have a standard plane poster to help guide them to the EFW ultrasound standard plane images. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial Intelligence feedback for ultrasound EFW standard plane images | Behavioral | AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation. |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic accuracy | The accuracy in each group was defined as the percentage difference between estimated fetal weight and the sonographer expert EFW | 15 minutes |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measurement of the reaction time |
Ultrasound novice participants:
Inclusion Criteria:
Exclusion Criteria:
• Medical students who received formal fetal or abdominal training prior to the inclusion in this study.
Pregnant women;
Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Rigshospitalet | Copenhagen | 2100 | Denmark |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32588532 | Background | 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. | |
| 33220065 | Background |
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| ID | Term |
|---|---|
| D020567 | Fetal Weight |
| ID | Term |
|---|---|
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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The participants are allocated to one of three groups:
control group, feedback group 1 with black box AI or feedback group 2 with explainable AI feedback.
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The ultrasound images will receive quality scoring from an experienced fetal medicin consultant. Theese are blinded for which intervention the participant received.
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 |
| 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. |
| 36755050 | Background | 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. |
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| 33141345 | Background | 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. |
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| 33328049 | Background | 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. |
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