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AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.
Fu, Howell; Novak, Alex; Robert, Dennis; Kumar, Shamie; Tanamala, Swetha; Oke, Jason; Bhatia, Kanika; Shah, Ruchir; Romsauerova, Andrea; Das, Tilak; Espinosa, Abdalá; Grzeda, Mariusz Tadeusz; Narbone, Mariapaola; Dharmadhikari, Rahul; Harrison, Mark; Vimalesvaran, Kavitha; Gooch, Jane; Woznitza, Nicholas; Salik, Nabeeha; Campbell, Alan; Khan, Farhaan; Lowe, David J; Shuaib, Haris; Ather, Sarim.
Afiliación
  • Fu H; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Novak A; Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK Alex.Novak@ouh.nhs.uk.
  • Robert D; Qure.AI, Bangalore, India.
  • Kumar S; Qure.AI, Bangalore, India.
  • Tanamala S; Qure.AI, Bangalore, India.
  • Oke J; Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
  • Bhatia K; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Shah R; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Romsauerova A; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Das T; Department of Clinical Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Espinosa A; Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Grzeda MT; School of Biomedical Science, King's College London, London, UK.
  • Narbone M; Guy's and St Thomas' Hospitals NHS Trust, London, UK.
  • Dharmadhikari R; Northumbria Healthcare NHS Foundation Trust, Northumberland, UK.
  • Harrison M; Emergency Department, Northumbria Specialist Emergency Care Hospital, Cramlington, UK.
  • Vimalesvaran K; Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
  • Gooch J; College of Health, Psychology & Social Care, University of Derby, Derby, UK.
  • Woznitza N; Radiology Department, University College London Hospitals NHS Foundation Trust, London, UK.
  • Salik N; School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, UK.
  • Campbell A; RAIQC Ltd, Oxford, UK.
  • Khan F; Radiology Department, University College London Hospitals NHS Foundation Trust, London, UK.
  • Lowe DJ; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Shuaib H; NHS Greater Glasgow and Clyde, Glasgow, UK.
  • Ather S; Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
BMJ Open ; 14(2): e079824, 2024 Feb 12.
Article en En | MEDLINE | ID: mdl-38346874
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. TRIAL REGISTRATION NUMBER NCT06018545.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Medicina Estatal / Inteligencia Artificial Tipo de estudio: Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: BMJ Open Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Medicina Estatal / Inteligencia Artificial Tipo de estudio: Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: BMJ Open Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido