AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study

Journal article

Howell Fu, Alex Novak, Dennis Robert, Shamie Kumar, Swetha Tanamala, Jason Oke, Kanika Bhatia, Ruchir Shah, Andrea Romsauerova, Tilak Das, Abdalá Espinosa, Mariusz Tadeusz Grzeda, Mariapaola Narbone, Rahul Dharmadhikari, Mark Harrison, Kavitha Vimalesvaran, Jane Gooch, Nicholas Woznitza, Nabeeha Salik, Alan Campbell, Farhaan Khan, David J Lowe, Haris Shuaib and Sarim Ather 2024. AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study. BMJ Open. 14 (2), p. e079824. https://doi.org/10.1136/bmjopen-2023-079824
AuthorsHowell Fu, Alex Novak, Dennis Robert, Shamie Kumar, Swetha Tanamala, Jason Oke, Kanika Bhatia, Ruchir Shah, Andrea Romsauerova, Tilak Das, Abdalá Espinosa, Mariusz Tadeusz Grzeda, Mariapaola Narbone, Rahul Dharmadhikari, Mark Harrison, Kavitha Vimalesvaran, Jane Gooch, Nicholas Woznitza, Nabeeha Salik, Alan Campbell, Farhaan Khan, David J Lowe, Haris Shuaib and Sarim Ather
Abstract

Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.

Methods and analysis A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.

Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.

KeywordsAI; Artificial intelligence; CT scans; CT head scans; Radiology and imaging
Year2024
JournalBMJ Open
Journal citation14 (2), p. e079824
PublisherBMJ Publishing Group
ISSN2044-6055
Digital Object Identifier (DOI)https://doi.org/10.1136/bmjopen-2023-079824
Official URLhttps://bmjopen.bmj.com/content/14/2/e079824
FunderNIHR
Publication dates
Print12 Feb 2024
Publication process dates
Accepted28 Jan 2024
Deposited21 Feb 2024
Publisher's version
License
File Access Level
Open
Output statusPublished
Additional information

Trial registration number NCT06018545.

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Woznitza, N., Piper, K., Burke, S., Patel, K., Amin, S., Grayson, K. and Bothamley, G. 2014. Adult chest radiograph reporting by radiographers: Preliminary data from an in-house audit programme. Radiography. 20 (3), pp. 223-229. https://doi.org/10.1016/j.radi.2014.03.002
Optimizing patient care in radiology through team-working: A case study
Woznitza, N., Piper, K., Rowe, S. and West, C. 2014. Optimizing patient care in radiology through team-working: A case study. Radiography. 20 (3), pp. 258-263. https://doi.org/10.1016/j.radi.2014.02.007
Conceptualisation of the characteristics of advanced practitioners in the medical radiation profession
Smith, Tony, Harris, J., Woznitza, N., Marese, S. and Sale, C. 2015. Conceptualisation of the characteristics of advanced practitioners in the medical radiation profession. Journal of Medical Radiation Sciences. 62 (3), pp. 204-211. https://doi.org/10.1002/jmrs.115
e-Learning for Healthcare – Image Interpretation Project
Woznitza, N., e-Learning for Healthcare and College of Radiographers 2012. e-Learning for Healthcare – Image Interpretation Project. e-Learning for Healthcare.
An unusual cause of respiratory distress
Woznitza, N., Datsopolous, S., Shukla, R. and Aladangady, N. 2012. An unusual cause of respiratory distress. Archives of Disease in Childhood. 97, pp. A12-A13. https://doi.org/10.1136/archdischild-2012-301885.30
Chest X-Ray interpretation: agreement between consultant radiologists and a reporting radiographer in clinical practice in the United Kingdom
Woznitza, N., Piper, K., Burke, S., Patel, K., Amin, S. and Grayson, K. 2013. Chest X-Ray interpretation: agreement between consultant radiologists and a reporting radiographer in clinical practice in the United Kingdom.
Radiographer clinical reporting
Woznitza, N. 2014. Radiographer clinical reporting.
Commentary: radiographer reporting
Woznitza, N. 2014. Commentary: radiographer reporting. Journal of Medical Radiation Sciences. 61 (2), pp. 66-68. https://doi.org/10.1002/jmrs.51
Do you see what I see? Clinical review of discordant chest x-ray consultant radiologist and radiographer interpretations
Woznitza, N., Piper, K., Bothamley, G. and Burke, S. 2014. Do you see what I see? Clinical review of discordant chest x-ray consultant radiologist and radiographer interpretations.
Optimizing patient care in radiology through team-working: audit data
Woznitza, N. 2014. Optimizing patient care in radiology through team-working: audit data.
Optimizing neonatal X-ray quality: results of an audit
Woznitza, N., Hayes, N., Malshava, N. and McGuinness, D. 2014. Optimizing neonatal X-ray quality: results of an audit.
Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer
Woznitza, N., Piper, K., Aladangady, N., Iliadis, K., Prakash, R. and Santos, R. 2014. Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer.
Advanced radiographic practice in adult chest imaging
Woznitza, N. 2014. Advanced radiographic practice in adult chest imaging.
Can reporting radiographers produce clinically relevant reports?
Woznitza, N. and Piper, K. 2011. Can reporting radiographers produce clinically relevant reports?