1
|
Vacek A, Mair G, White P, Bath PM, Muir KW, Al-Shahi Salman R, Martin C, Dye D, Chappell FM, von Kummer R, Macleod M, Sprigg N, Wardlaw JM. Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT. J Stroke Cerebrovasc Dis 2024; 33:107512. [PMID: 38007987 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/25/2023] [Accepted: 11/21/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation. METHODS We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI). RESULTS From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension. CONCLUSIONS Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial.
Collapse
Affiliation(s)
- Adam Vacek
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Grant Mair
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK.
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, UK
| | - Keith W Muir
- School of Psychology & Neuroscience, University of Glasgow, UK
| | - Rustam Al-Shahi Salman
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Chloe Martin
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - David Dye
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Francesca M Chappell
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Rüdiger von Kummer
- Department of Neuroradiology, University Hospital, Technische Universität Dresden, Germany
| | - Malcolm Macleod
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| |
Collapse
|
2
|
Mallon D, Fallon M, Blana E, McNamara C, Menon A, Ip CL, Garnham J, Yousry T, Cowley P, Simister R, Doig D. Real-world evaluation of Brainomix e-Stroke software. Stroke Vasc Neurol 2023:svn-2023-002859. [PMID: 38164621 DOI: 10.1136/svn-2023-002859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND PURPOSE Brainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to improve the speed and accuracy of diagnosis, real-world validation is essential. The aim of this study was to prospectively evaluate the performance of Brainomix e-Stroke in an unselected cohort of patients with suspected acute ischaemic stroke. METHODS The study cohort included all patients admitted to the University College London Hospital Hyperacute Stroke Unit between October 2021 and April 2022. For e-ASPECTS and e-CTA, the ground truth was determined by a neuroradiologist with access to all clinical and imaging data. For e-CTP, the values of the core infarct and ischaemic penumbra were compared with those derived from syngo.via, an alternate software used at our institution. RESULTS 1163 studies were performed in 551 patients admitted during the study period. Of these, 1130 (97.2%) were successfully processed by e-Stroke in an average of 4 min. For identifying acute middle cerebral artery territory ischaemia, e-ASPECTS had an accuracy of 77.0% and was more specific (83.5%) than sensitive (58.6%). The accuracy for identifying hyperdense thrombus was lower (69.1%), which was mainly due to many false positives (positive predictive value of 22.9%). Identification of acute haemorrhage was highly accurate (97.8%) with a sensitivity of 100% and a specificity of 97.6%; false positives were typically caused by areas of calcification. The accuracy of e-CTA for large vessel occlusions was 91.5%. The core infarct and ischaemic penumbra volumes provided by e-CTP strongly correlated with those provided by syngo.via (ρ=0.804-0.979). CONCLUSION Brainomix e-Stroke software provides rapid and reliable analysis of CT imaging in the acute stroke setting although, in line with the manufacturer's guidance, it should be used as an adjunct to expert interpretation rather than a standalone decision-making tool.
Collapse
Affiliation(s)
- Dermot Mallon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Matthew Fallon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Eirini Blana
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Cillian McNamara
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Arathi Menon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Chak Lam Ip
- Comprehensive Stroke Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Jack Garnham
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Tarek Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Peter Cowley
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Robert Simister
- UCL Queen Square Institute of Neurology, London, UK
- Comprehensive Stroke Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - David Doig
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
| |
Collapse
|
3
|
Wechsler LR, Adeoye O, Alemseged F, Bahr-Hosseini M, Deljkich E, Favilla C, Fisher M, Grotta J, Hill MD, Kamel H, Khatri P, Lyden P, Mirza M, Nguyen TN, Samaniego E, Schwamm L, Selim M, Silva G, Yavagal DR, Yenari MA, Zachrison KS, Boltze J, Yaghi S. Most Promising Approaches to Improve Stroke Outcomes: The Stroke Treatment Academic Industry Roundtable XII Workshop. Stroke 2023; 54:3202-3213. [PMID: 37886850 DOI: 10.1161/strokeaha.123.044279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023]
Abstract
The Stroke Treatment Academic Industry Roundtable XII included a workshop to discuss the most promising approaches to improve outcome from acute stroke. The workshop brought together representatives from academia, industry, and government representatives. The discussion examined approaches in 4 epochs: pre-reperfusion, reperfusion, post-reperfusion, and access to acute stroke interventions. The participants identified areas of priority for developing new and existing treatments and approaches to improve stroke outcomes. Although many advances in acute stroke therapy have been achieved, more work is necessary for reperfusion therapies to benefit the most possible patients. Prioritization of promising approaches should help guide the use of resources and investigator efforts.
Collapse
Affiliation(s)
- Lawrence R Wechsler
- University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, PA (L.R.W.)
| | - Opeolu Adeoye
- Washington University School of Medicine, St. Louis, MO (O.A.)
| | | | | | | | | | - Marc Fisher
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (M.F.)
| | | | | | - Hooman Kamel
- Weill Cornel School of Medicine, New York, NY (H.K.)
| | - Pooja Khatri
- University of Cincinnati Medical Center, OH (P.K.)
| | - Patrick Lyden
- University of Southern California, Los Angeles, CA (P.L.)
| | | | | | | | - Lee Schwamm
- Massachusetts General Hospital, Boston (L.S.)
| | - Magdy Selim
- Beth Israel Deaconess Medical Center, Boston, MA (M.S.)
| | | | | | | | | | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, United Kingdom (J.B.)
| | | |
Collapse
|
4
|
Kobeissi H, Kallmes DF, Benson J, Nagelschneider A, Madhavan A, Messina SA, Schwartz K, Campeau N, Carr CM, Nasr DM, Braksick S, Scharf EL, Klaas J, Woodhead ZVJ, Harston G, Briggs J, Joly O, Gerry S, Kuhn AL, Kostas AA, Nael K, AbdalKader M, Kadirvel R, Brinjikji W. Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study. Front Neurol 2023; 14:1221255. [PMID: 37745671 PMCID: PMC10513025 DOI: 10.3389/fneur.2023.1221255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Background The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) brain scans from patients with AIS affecting the middle cerebral artery (MCA) territory and generate an ASPECTS. This study aimed to evaluate the impact of e-ASPECTS (Brainomix, Oxford, UK) on the performance of US physicians compared to a consensus ground truth. Methods The study used a multi-reader, multi-case design. A total of 10 US board-certified physicians (neurologists and neuroradiologists) scored 54 NCCT brain scans of patients with AIS affecting the MCA territory. Each reader scored each scan on two occasions: once with and once without reference to the e-ASPECTS software, in random order. Agreement with a reference standard (expert consensus read with reference to follow-up imaging) was evaluated with and without software support. Results A comparison of the area under the curve (AUC) for each reader showed a significant improvement from 0.81 to 0.83 (p = 0.028) with the support of the e-ASPECTS tool. The agreement of reader ASPECTS scoring with the reference standard was improved with e-ASPECTS compared to unassisted reading of scans: Cohen's kappa improved from 0.60 to 0.65, and the case-based weighted Kappa improved from 0.70 to 0.81. Conclusion Decision support with the e-ASPECTS software significantly improves the accuracy of ASPECTS scoring, even by expert US neurologists and neuroradiologists.
Collapse
Affiliation(s)
- Hassan Kobeissi
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - David F. Kallmes
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - John Benson
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Ajay Madhavan
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Kara Schwartz
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Norbert Campeau
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Carrie M. Carr
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Deena M. Nasr
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Sherri Braksick
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Eugene L. Scharf
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - James Klaas
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | | | - George Harston
- Brainomix Limited, Oxford, United Kingdom
- Acute Stroke Service, Oxford University Hospitals NHSFT, Oxford, United Kingdom
| | - James Briggs
- Brainomix Limited, Oxford, United Kingdom
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | | | - Stephen Gerry
- Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Anna L. Kuhn
- Division of Neurointerventional Radiology, Department of Radiology, UMass Medical Center, Worcester, MA, United States
| | - Angelos A. Kostas
- Huntington Hospital and Hill Medical Imaging, Pasadena, CA, United States
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA, United States
| | - Mohamad AbdalKader
- Department of Radiology, Boston Medical Center, Boston, MA, United States
| | - Ramanathan Kadirvel
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
| | - Waleed Brinjikji
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
| |
Collapse
|
5
|
Yang Y, Huan X, Guo D, Wang X, Niu S, Li K. Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study. LA RADIOLOGIA MEDICA 2023; 128:1103-1115. [PMID: 37464200 DOI: 10.1007/s11547-023-01683-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 07/10/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth. MATERIAL AND METHODS Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results. RESULTS 296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion. CONCLUSIONS CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.
Collapse
Affiliation(s)
- Yongwei Yang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
- Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China
| | - Xinyue Huan
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Xiaolin Wang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Shengwen Niu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Kunhua Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
| |
Collapse
|
6
|
Lambert J, Demeestere J, Dewachter B, Cockmartin L, Wouters A, Symons R, Boomgaert L, Vandewalle L, Scheldeman L, Demaerel P, Lemmens R. Performance of Automated ASPECTS Software and Value as a Computer-Aided Detection Tool. AJNR Am J Neuroradiol 2023; 44:894-900. [PMID: 37500286 PMCID: PMC10411841 DOI: 10.3174/ajnr.a7956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/14/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND AND PURPOSE ASPECTS quantifies early ischemic changes in anterior circulation stroke on NCCT but has interrater variability. We examined the agreement of conventional and automated ASPECTS and studied the value of computer-aided detection. MATERIALS AND METHODS We retrospectively collected imaging data from consecutive patients with acute ischemic stroke with large-vessel occlusion undergoing thrombectomy. Five raters scored conventional ASPECTS on baseline NCCTs, which were also processed by RAPID software. Conventional and automated ASPECTS were compared with a consensus criterion standard. We determined the agreement over the full ASPECTS range as well as dichotomized, reflecting thrombectomy eligibility according to the guidelines (ASPECTS 0-5 versus 6-10). Raters subsequently scored ASPECTS on the same NCCTs with assistance of the automated ASPECTS outputs, and agreement was obtained. RESULTS For the total of 175 cases, agreement among raters individually and the criterion standard varied from fair to good (weighted κ = between 0.38 and 0.76) and was moderate (weighted κ = 0.59) for the automated ASPECTS. The agreement of all raters individually versus the criterion standard improved with software assistance, as did the interrater agreement (overall Fleiss κ = 0.15-0.23; P < .001 and .39 to .55; P = .01 for the dichotomized ASPECTS). CONCLUSIONS Automated ASPECTS had agreement with the criterion standard similar to that of conventional ASPECTS. However, including automated ASPECTS during the evaluation of NCCT in acute stroke improved the agreement with the criterion standard and improved interrater agreement, which could, therefore, result in more uniform scoring in clinical practice.
Collapse
Affiliation(s)
- J Lambert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
| | - J Demeestere
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - B Dewachter
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - L Cockmartin
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - A Wouters
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - R Symons
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Imelda Hospital (R.S.), Bonheiden, Belgium
| | - L Boomgaert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - L Vandewalle
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - L Scheldeman
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - P Demaerel
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - R Lemmens
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| |
Collapse
|
7
|
Mair G, White P, Bath PM, Muir K, Martin C, Dye D, Chappell F, von Kummer R, Macleod M, Sprigg N, Wardlaw JM. Accuracy of artificial intelligence software for CT angiography in stroke. Ann Clin Transl Neurol 2023; 10:1072-1082. [PMID: 37208850 PMCID: PMC10351662 DOI: 10.1002/acn3.51790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/01/2023] [Indexed: 05/21/2023] Open
Abstract
OBJECTIVE Software developed using artificial intelligence may automatically identify arterial occlusion and provide collateral vessel scoring on CT angiography (CTA) performed acutely for ischemic stroke. We aimed to assess the diagnostic accuracy of e-CTA by Brainomix™ Ltd by large-scale independent testing using expert reading as the reference standard. METHODS We identified a large clinically representative sample of baseline CTA from 6 studies that recruited patients with acute stroke symptoms involving any arterial territory. We compared e-CTA results with masked expert interpretation of the same scans for the presence and location of laterality-matched arterial occlusion and/or abnormal collateral score combined into a single measure of arterial abnormality. We tested the diagnostic accuracy of e-CTA for identifying any arterial abnormality (and in a sensitivity analysis compliant with the manufacturer's guidance that software only be used to assess the anterior circulation). RESULTS We include CTA from 668 patients (50% female; median: age 71 years, NIHSS 9, 2.3 h from stroke onset). Experts identified arterial occlusion in 365 patients (55%); most (343, 94%) involved the anterior circulation. Software successfully processed 545/668 (82%) CTAs. The sensitivity, specificity and diagnostic accuracy of e-CTA for detecting arterial abnormality were each 72% (95% CI = 66-77%). Diagnostic accuracy was non-significantly improved in a sensitivity analysis excluding occlusions from outside the anterior circulation (76%, 95% CI = 72-80%). INTERPRETATION Compared to experts, the diagnostic accuracy of e-CTA for identifying acute arterial abnormality was 72-76%. Users of e-CTA should be competent in CTA interpretation to ensure all potential thrombectomy candidates are identified.
Collapse
Affiliation(s)
- Grant Mair
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne Hospitals NHS TrustNewcastle upon TyneUK
| | - Philip M. Bath
- Stroke Trials Unit, Mental Health & Clinical NeuroscienceUniversity of NottinghamNottinghamUK
| | - Keith Muir
- Institute of Neuroscience & Psychology, University of GlasgowGlasgowUK
| | - Chloe Martin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - David Dye
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Rüdiger von Kummer
- Department of NeuroradiologyUniversity Hospital, Technische Universität DresdenDresdenGermany
| | - Malcolm Macleod
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Nikola Sprigg
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne Hospitals NHS TrustNewcastle upon TyneUK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute Centre at the University of EdinburghEdinburghUK
| | | |
Collapse
|
8
|
Gottesman RF, Latour L. What's the Future of Vascular Neurology? Neurotherapeutics 2023; 20:605-612. [PMID: 37129762 PMCID: PMC10275820 DOI: 10.1007/s13311-023-01374-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2023] [Indexed: 05/03/2023] Open
Abstract
The field of vascular neurology has made tremendous advances over the last several decades, with major shifts in diagnosis, treatment, prevention, and rehabilitation of patients with stroke. Furthermore, the individuals who are providing the care represent a different cohort than those who were caring for stroke patients 30 years ago, with the increasing need for rapid decision-making for acute interventions and a larger workforce being needed to provide the many complicated aspects of care of stroke patients. Understanding the history of the field is critical before one can speculate about its future directions. In summarizing some of the past massive shifts in the past few decades, this review will discuss future opportunities and future challenges and will introduce the rest of this special issue focusing on vascular neurology in a post-thrombectomy era. Although thrombolysis and thrombectomy remain a major part of ischemic stroke management and care, in the coming years, there will likely be further modifications in how we provide the care, who provides it, how we train those individuals who provide it, where it is provided, and what data inform early management decisions.
Collapse
Affiliation(s)
- Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA.
| | - Lawrence Latour
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
9
|
Mair G, White P, Bath P, Muir KW, Chappell FM, Wardlaw JM. Reply to "Independent Confirmation". Ann Neurol 2023; 93:425. [PMID: 36507582 DOI: 10.1002/ana.26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Grant Mair
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Phil White
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Philip Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Keith W Muir
- School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK
| | | | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| |
Collapse
|
10
|
Herweh C, Nagel S. Independent Confirmation. Ann Neurol 2023; 93:424-425. [PMID: 36511828 DOI: 10.1002/ana.26578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/08/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Christian Herweh
- Department of Neuroradiology/Neurology, Heidelberg Medical School, Heidelberg, Germany
| | - Simon Nagel
- Department of Neurology, Heidelberg University Heidelberg, Heidelberg, Germany
- Department of Neurology, Klinikum der Stadt Ludwigshafen, Ludwigshafen, Germany
| |
Collapse
|