AI Assisted Reader Evaluation in Acute Computed Tomography (CT) Head Interpretation

Acute Ischemic Stroke Clinical Trial

— AI-REACT  

Official title:

AI Assisted Reader Evaluation in Acute CT Head Interpretation

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06018545
• Other study ID #: 310995 - A
• Status: Active, not recruiting
• Start date: June 1, 2023
• Est. completion date: September 1, 2024

Study information

• Verified date: August 2023
• Source: Oxford University Hospitals NHS Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838). The purpose of the study is to assess the impact of an Artificial Intelligence (AI) tool called qER 2.0 EU on the performance of readers, including general radiologists, emergency medicine clinicians, and radiographers, in interpreting non-contrast CT head scans. The study aims to evaluate the changes in accuracy, review time, and diagnostic confidence when using the AI tool. It also seeks to provide evidence on the diagnostic performance of the AI tool and its potential to improve efficiency and patient care in the context of the National Health Service (NHS). The study will use a dataset of 150 CT head scans, including both control cases and abnormal cases with specific abnormalities. The results of this study will inform larger follow-up studies in real-life Emergency Department (ED) settings.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 33
• Est. completion date: September 1, 2024
• Est. primary completion date: September 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice

Exclusion Criteria:

• Neuroradiologists.
• Non-radiologist groups: Clinicians with previous formal postgraduate CT reporting training
• Emergency Medicine group: Clinicians with previous career in radiology/neurosurgery to registrar level

Related Conditions & MeSH terms

• Acute Ischemic Stroke
• Brain Edema
• Brain Injuries
• Cerebral Edema
• Cerebral Infarction
• Cerebral Injury
• Hemorrhage
• Hydrocephalus
• Infarction
• Intracranial Hemorrhages
• Ischemic Stroke

Intervention

Other:
• Ground truthing
Two Consultant neuroradiologists will independently review the images to establish the 'ground truth' findings on the CT scans which will be used as the reference standard. In the case of disagreement, a third senior neuroradiologist's opinion will be sought for arbitration.
• Reading
All 30 readers will review all 150 cases, in each of two study phases. The readers will provide their opinion on the presence or absence of some acute abnormalities, including intracranial haemorrhage, infarct, midline shift and fracture. They will provide a confidence in their diagnosis (10-point visual analogue scale), and a single click point to mark the location of each abnormality that they consider as being present. The time taken for each scan will be automatically recorded.

Locations

Country	Name	City	State
United Kingdom	NHS Greater Glasgow and Clyde	Glasgow
United Kingdom	Guy's & St Thomas NHS Foundation Trust	London
United Kingdom	Northumbria Healthcare NHS Foundation Trust	Newcastle Upon Tyne
United Kingdom	Oxford University Hospitals NHS Foundation Trust	Oxford	Oxfordshire

Sponsors (1)

Lead Sponsor	Collaborator
Oxford University Hospitals NHS Trust

Country where clinical trial is conducted

United Kingdom, 

References & Publications (21)

Andralojc LE, Kim DH, Edwards AJ. Diagnostic accuracy of a decision-support software for the detection of intracranial large-vessel occlusion in CT angiography. Clin Radiol. 2023 Apr;78(4):e313-e318. doi: 10.1016/j.crad.2022.10.017. Epub 2023 Jan 11. — View Citation

Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018 Apr 4;1:9. doi: 10.1038/s41746-017-0015-z. eCollection 2018. — View Citation

Chan J, Fan KS, Mak TLA, Loh SY, Ng SWY, Adapala R. Pre-Operative Imaging can Reduce Negative Appendectomy Rate in Acute Appendicitis. Ulster Med J. 2020 Jan;89(1):25-28. Epub 2020 Feb 18. — View Citation

Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018 Dec 1;392(10162):2388-2396. doi: 10.1016/S0140-6736(18)31645-3. Epub 2018 Oct 11. — View Citation

Davis MA, Rao B, Cedeno PA, Saha A, Zohrabian VM. Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography. Curr Probl Diagn Radiol. 2022 Jul-Aug;51(4):556-561. doi: 10.1067/j.cpradiol.2020.10.007. Epub 2020 Nov 15. — View Citation

Dyer T, Chawda S, Alkilani R, Morgan TN, Hughes M, Rasalingham S. Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans. Neuroradiology. 2022 Apr;64(4):735-743. doi: 10.1007/s00234-021-02826-4. Epub 2021 Oct 8. — View Citation

Finck T, Moosbauer J, Probst M, Schlaeger S, Schuberth M, Schinz D, Yigitsoy M, Byas S, Zimmer C, Pfister F, Wiestler B. Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography. Diagnostics (Basel). 2022 Feb 10;12(2):452. doi: 10.3390/diagnostics12020452. — View Citation

Greenhalgh R, Howlett DC, Drinkwater KJ. Royal College of Radiologists national audit evaluating the provision of imaging in the severely injured patient and compliance with national guidelines. Clin Radiol. 2020 Mar;75(3):224-231. doi: 10.1016/j.crad.2019.10.025. Epub 2019 Dec 19. — View Citation

Guo Y, He Y, Lyu J, Zhou Z, Yang D, Ma L, Tan HT, Chen C, Zhang W, Hu J, Han D, Ding G, Liu S, Qiao H, Xu F, Lou X, Dai Q. Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study. Lancet Digit Health. 2022 Aug;4(8):e584-e593. doi: 10.1016/S2589-7500(22)00090-5. Epub 2022 Jun 17. Erratum In: Lancet Digit Health. 2022 Aug;4(8):e572. — View Citation

Hillis SL, Obuchowski NA, Schartz KM, Berbaum KS. A comparison of the Dorfman-Berbaum-Metz and Obuchowski-Rockette methods for receiver operating characteristic (ROC) data. Stat Med. 2005 May 30;24(10):1579-607. doi: 10.1002/sim.2024. — View Citation

Huang SC, Pareek A, Jensen M, Lungren MP, Yeung S, Chaudhari AS. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digit Med. 2023 Apr 26;6(1):74. doi: 10.1038/s41746-023-00811-0. — View Citation

Juszczyk K, Ireland K, Thomas B, Kroon HM, Hollington P. Reduction in hospital admissions with an early computed tomography scan: results of an outpatient management protocol for uncomplicated acute diverticulitis. ANZ J Surg. 2019 Sep;89(9):1085-1090. doi: 10.1111/ans.15285. Epub 2019 Jun 17. — View Citation

Lee JY, Kim JS, Kim TY, Kim YS. Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm. Sci Rep. 2020 Nov 25;10(1):20546. doi: 10.1038/s41598-020-77441-z. — View Citation

Lin E, Yuh EL. Computational Approaches for Acute Traumatic Brain Injury Image Recognition. Front Neurol. 2022 Mar 9;13:791816. doi: 10.3389/fneur.2022.791816. eCollection 2022. — View Citation

Mallon DH, Taylor EJR, Vittay OI, Sheeka A, Doig D, Lobotesis K. Comparison of automated ASPECTS, large vessel occlusion detection and CTP analysis provided by Brainomix and RapidAI in patients with suspected ischaemic stroke. J Stroke Cerebrovasc Dis. 2022 Oct;31(10):106702. doi: 10.1016/j.jstrokecerebrovasdis.2022.106702. Epub 2022 Aug 19. — View Citation

Obuchowski NA. Sample size tables for receiver operating characteristic studies. AJR Am J Roentgenol. 2000 Sep;175(3):603-8. doi: 10.2214/ajr.175.3.1750603. — View Citation

Sheth SA, Giancardo L, Colasurdo M, Srinivasan VM, Niktabe A, Kan P. Machine learning and acute stroke imaging. J Neurointerv Surg. 2023 Feb;15(2):195-199. doi: 10.1136/neurintsurg-2021-018142. Epub 2022 May 25. — View Citation

Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke. 2022 Jul;53(7):2393-2403. doi: 10.1161/STROKEAHA.121.036204. Epub 2022 Apr 20. — View Citation

Warman R, Warman A, Warman P, Degnan A, Blickman J, Chowdhary V, Dash D, Sangal R, Vadhan J, Bueso T, Windisch T, Neves G. Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage. Cureus. 2022 Oct 13;14(10):e30264. doi: 10.7759/cureus.30264. eCollection 2022 Oct. — View Citation

Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging. J Neurointerv Surg. 2021 Apr;13(4):369-378. doi: 10.1136/neurintsurg-2020-017099. Epub 2021 Jan 21. — View Citation

Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med. 2018 Nov 6;15(11):e1002683. doi: 10.1371/journal.pmed.1002683. eCollection 2018 Nov. — View Citation

* Note: There are 21 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.	Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	Reader performance: Positive and negative predictive value, comparative between with and without AI assistance.	Reader performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV), with and without AI assistance.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	Reader performance: Area Under Receiver Operating Characteristic Curve (AUROC), comparative between with and without AI assistance.	Reader performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC), with and without AI assistance.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	Reader speed: Mean time taken to review a scan, with versus without AI assistance.	Reader speed will be evaluated as the man time taken to review a scan, using time unite of seconds.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	Reader confidence: Self-reported diagnostic confidence on a 10 point visual analogue scale, with vs without AI assistance.	On the reading platform (RAIQC), one of the questions asks the level of confidence that the participant has in their diagnostic opinion. The question offers a scale of 1 to 10, where 1 is not confident, and 10 is highly confident.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	qER (AI algorithm) performance: Sensitivity and specificity	qER performance will be evaluated as sensitivity, specificity.	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	qER (AI algorithm) performance: Positive and negative predictive value.	qER performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV).	During 6 weeks, which is the period for reading or reviewing the cases/scans.
Primary	qER (AI algorithm) performance: Area Under Receiver Operating Characteristic Curve (AUROC).	qER performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC)	During 6 weeks, which is the period for reading or reviewing the cases/scans.

External Links

•   National Institute for Health and Care Excellence. Artificial intelligence for analysing CT brain scans. 2020.
•   Richards M. Diagnostics: Recovery and Renewal - Report of the Independent Review of Diagnostic Services for NHS England. NHS England 2022.
•   Royal College of Radiologists. Clinical radiology UK workforce census 2019 report. Royal College of Radiologists 2020.