Ryan A Rava1, Samantha E Seymour2, Kenneth V Snyder3, Muhammad Waqas4, Jason M Davies5, Elad I Levy3, Adnan H Siddiqui3, Ciprian N Ionita6. 1. Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA. Electronic address: ryanrava@buffalo.edu. 2. Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA. 3. Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA. 4. Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA. 5. Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Department of Bioinformatics, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA. 6. Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA.
Abstract
BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, and difficult because of complex neurovasculature. This study assessed the ability of multiple artificial intelligence algorithms in determining collateral flow of patients with AIS. METHODS: Two hundred patients with AIS between March 2019 and January 2020 were included in this retrospective study. Peak arterial computed tomography perfusion volumes were used to assess collateral scores. Neural networks were developed for dichotomized (≥50% or <50%) and multiclass (0% filling, 0%-50% filling, 50%-100% filling, or 100% filling) collateral scoring. Maximum intensity projections from axial and anteroposterior (AP) views were synthesized for each bone subtracted three-dimensional volume and used as network inputs separately and together, along with three-dimensional data. Training:testing:validation splits of 60:30:10 and 20 iterations of Monte Carlo cross-validation were used. Network performance was assessed using 95% confidence intervals of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The axial and AP input combination provided the most accurate results for dichotomized classification: accuracy, 0.85 ± 0.01; sensitivity, 0.88 ± 0.02; specificity, 0.82 ± 0.03; PPV, 0.86 ± 0.02; and NPV, 0.83 ± 0.03. Similarly, the axial and AP input combination provided the best results for multiclass classification: accuracy, 0.80 ± 0.01; sensitivity, 0.64 ± 0.01; specificity, 0.85 ± 0.01; PPV, 0.65 ± 0.02; and NPV, 0.85 ± 0.01. CONCLUSIONS: This study reports one of the first artificial intelligence-based algorithms capable of accurately and efficiently assessing collateral flow of patients with AIS. This automated method for determining collateral filling could streamline clinical workflow, reduce bias, and aid in clinical decision making for determining reperfusion-eligible patients.
BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, and difficult because of complex neurovasculature. This study assessed the ability of multiple artificial intelligence algorithms in determining collateral flow of patients with AIS. METHODS: Two hundred patients with AIS between March 2019 and January 2020 were included in this retrospective study. Peak arterial computed tomography perfusion volumes were used to assess collateral scores. Neural networks were developed for dichotomized (≥50% or <50%) and multiclass (0% filling, 0%-50% filling, 50%-100% filling, or 100% filling) collateral scoring. Maximum intensity projections from axial and anteroposterior (AP) views were synthesized for each bone subtracted three-dimensional volume and used as network inputs separately and together, along with three-dimensional data. Training:testing:validation splits of 60:30:10 and 20 iterations of Monte Carlo cross-validation were used. Network performance was assessed using 95% confidence intervals of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The axial and AP input combination provided the most accurate results for dichotomized classification: accuracy, 0.85 ± 0.01; sensitivity, 0.88 ± 0.02; specificity, 0.82 ± 0.03; PPV, 0.86 ± 0.02; and NPV, 0.83 ± 0.03. Similarly, the axial and AP input combination provided the best results for multiclass classification: accuracy, 0.80 ± 0.01; sensitivity, 0.64 ± 0.01; specificity, 0.85 ± 0.01; PPV, 0.65 ± 0.02; and NPV, 0.85 ± 0.01. CONCLUSIONS: This study reports one of the first artificial intelligence-based algorithms capable of accurately and efficiently assessing collateral flow of patients with AIS. This automated method for determining collateral filling could streamline clinical workflow, reduce bias, and aid in clinical decision making for determining reperfusion-eligible patients.
Authors: Jeff Joseph; Benjamin Weppner; Nandor K Pinter; Mohammad Mahdi Shiraz Bhurwani; Andre Monteiro; Ammad Baig; Jason Davies; Adnan Siddiqui; Ciprian N Ionita Journal: Proc SPIE Int Soc Opt Eng Date: 2022-04-04
Authors: Dennis Swetz; Samantha E Seymour; Ryan A Rava; Mohammad Mahdi Shiraz Bhurwani; Andre Monteiro; Ammad A Baig; Muhammad Waqas; Kenneth V Snyder; Elad I Levy; Jason M Davies; Adnan H Siddiqui; Ciprian N Ionita Journal: Proc SPIE Int Soc Opt Eng Date: 2022-04-04
Authors: Narendra N Khanna; Mahesh Maindarkar; Ajit Saxena; Puneet Ahluwalia; Sudip Paul; Saurabh K Srivastava; Elisa Cuadrado-Godia; Aditya Sharma; Tomaz Omerzu; Luca Saba; Sophie Mavrogeni; Monika Turk; John R Laird; George D Kitas; Mostafa Fatemi; Al Baha Barqawi; Martin Miner; Inder M Singh; Amer Johri; Mannudeep M Kalra; Vikas Agarwal; Kosmas I Paraskevas; Jagjit S Teji; Mostafa M Fouda; Gyan Pareek; Jasjit S Suri Journal: Diagnostics (Basel) Date: 2022-05-17
Authors: Ștefan Busnatu; Adelina-Gabriela Niculescu; Alexandra Bolocan; George E D Petrescu; Dan Nicolae Păduraru; Iulian Năstasă; Mircea Lupușoru; Marius Geantă; Octavian Andronic; Alexandru Mihai Grumezescu; Henrique Martins Journal: J Clin Med Date: 2022-04-18 Impact factor: 4.964
Authors: Jasjit S Suri; Mahesh A Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M Singh; Paramjit S Chadha; Monika Turk; Amer Johri; Narendra N Khanna; Klaudija Viskovic; Sofia Mavrogeni; John R Laird; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanase D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Padukode R Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Kosmas I Paraskevas; Mannudeep Kalra; Zoltán Ruzsa; Mostafa M Fouda Journal: Diagnostics (Basel) Date: 2022-06-24