1
|
Abdollahifard S, Farrokhi A, Mowla A, Liebeskind DS. Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements. Neurol Clin 2024; 42:633-650. [PMID: 38937033 DOI: 10.1016/j.ncl.2024.03.001] [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] [Indexed: 06/29/2024]
Abstract
Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medicine is not an exception to the growing usage of AI in various scientific fields. Vascular and interventional neurology deal with diseases that require early diagnosis and appropriate intervention, which are crucial to saving patients' lives. In these settings, AI can be an extra pair of hands for physicians or in conditions where there is a shortage of clinical experts. In this article, the authors have reviewed the common metrics used in interpreting the performance of models and common algorithms used in this field.
Collapse
Affiliation(s)
- Saeed Abdollahifard
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Research Center for Neuromodulation and Pain, Shiraz, Iran
| | | | - Ashkan Mowla
- Division of Stroke and Endovascular Neurosurgery, Department of Neurological Surgery, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | - David S Liebeskind
- UCLA Department of Neurology, Neurovascular Imaging Research Core, UCLA Comprehensive Stroke Center, University of California Los Angeles(UCLA), CA, USA.
| |
Collapse
|
2
|
Luo J, Dai P, He Z, Huang Z, Liao S, Liu K. Deep learning models for ischemic stroke lesion segmentation in medical images: A survey. Comput Biol Med 2024; 175:108509. [PMID: 38677171 DOI: 10.1016/j.compbiomed.2024.108509] [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: 11/02/2023] [Revised: 02/09/2024] [Accepted: 04/21/2024] [Indexed: 04/29/2024]
Abstract
This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in medical images such as MRI and CT scans is crucial for diagnosis, treatment planning and prognosis. This paper first introduces common imaging modalities used for stroke diagnosis, discussing their capabilities in imaging lesions at different disease stages from the acute to chronic stage. It then reviews three major public benchmark datasets for evaluating stroke segmentation algorithms: ATLAS, ISLES and AISD, highlighting their key characteristics. The paper proceeds to provide an overview of foundational deep learning architectures for medical image segmentation, including CNN-based and transformer-based models. It summarizes recent innovations in adapting these architectures to the task of stroke lesion segmentation across the three datasets, analyzing their motivations, modifications and results. A survey of loss functions and data augmentations employed for this task is also included. The paper discusses various aspects related to stroke segmentation tasks, including prior knowledge, small lesions, and multimodal fusion, and then concludes by outlining promising future research directions. Overall, this comprehensive review covers critical technical developments in the field to support continued progress in automated stroke lesion segmentation.
Collapse
Affiliation(s)
- Jialin Luo
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Zhuang He
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Shenghui Liao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Kun Liu
- Brain Hospital of Hunan Province (The Second People's Hospital of Hunan Province), Changsha, Hunan, China
| |
Collapse
|
3
|
Yang Y, Guo Y. Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network. Front Neurol 2024; 15:1394879. [PMID: 38765270 PMCID: PMC11099238 DOI: 10.3389/fneur.2024.1394879] [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: 03/02/2024] [Accepted: 04/12/2024] [Indexed: 05/21/2024] Open
Abstract
Objectives This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. Design In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. Results The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. Conclusion The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.
Collapse
Affiliation(s)
- Yingjian Yang
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
- Shenzhen Lanmage Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Yingwei Guo
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
| |
Collapse
|
4
|
Jeong H, Lim H, Yoon C, Won J, Lee GY, de la Rosa E, Kirschke JS, Kim B, Kim N, Kim C. Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01099-6. [PMID: 38693333 DOI: 10.1007/s10278-024-01099-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 05/03/2024]
Abstract
Ischemic stroke segmentation at an acute stage is vital in assessing the severity of patients' impairment and guiding therapeutic decision-making for reperfusion. Although many deep learning studies have shown attractive performance in medical segmentation, it is difficult to use these models trained on public data with private hospitals' datasets. Here, we demonstrate an ensemble model that employs two different multimodal approaches for generalization, a more effective way to perform on external datasets. First, after we jointly train a segmentation model on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) MR modalities, the model is inferred on the DWI images. Second, a channel-wise segmentation model is trained by concatenating the DWI and ADC images as input, and then is inferred using both MR modalities. Before training with ischemic stroke data, we utilized BraTS 2021, a public brain tumor dataset, for transfer learning. An extensive ablation study evaluates which strategy learns better representations for ischemic stroke segmentation. In our study, nnU-Net well-known for robustness is selected as our baseline model. Our proposed method is evaluated on three different datasets: the Asan Medical Center (AMC) I and II, and the 2022 Ischemic Stroke Lesion Segmentation (ISLES). Our experiments are widely validated over a large, multi-center, and multi-scanner dataset with a huge amount of 846 scans. Not only stroke lesion models can benefit from transfer learning using brain tumor data, but combining the MR modalities using different training schemes also highly improves segmentation performance. The method achieved a top-1 ranking in the ongoing ISLES'22 challenge and performed particularly well on lesion-wise metrics of interest to neuroradiologists, achieving a Dice coefficient of 78.69% and a lesion-wise F1 score of 82.46%. Also, the method was relatively robust on the AMC I (Dice, 60.35%; lesion-wise F1, 68.30%) and II (Dice; 74.12%; lesion-wise F1, 67.53%) datasets in different settings. The high segmentation accuracy of our proposed method could improve radiologists' ability to detect ischemic stroke lesions in MRI images. Our model weights and inference code are available on https://github.com/MDOpx/ISLES22-model-inference .
Collapse
Affiliation(s)
- Hyunsu Jeong
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Hyunseok Lim
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Chiho Yoon
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Jongjun Won
- Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Grace Yoojin Lee
- Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Informatics, Technical University of Munich, Neuroradiology Munich, Germany
| | - Jan S Kirschke
- Department of Informatics, Technical University of Munich, Neuroradiology Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechtsder Isar, Technical University of Munich, Munich, Germany
| | - Bumjoon Kim
- Department of Biomedical Engineering, Convergence Medicine, Radiology, Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Namkug Kim
- Department of Biomedical Engineering, Convergence Medicine, Radiology, Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Chulhong Kim
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.
| |
Collapse
|
5
|
Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [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: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
Collapse
Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| |
Collapse
|
6
|
Ghosh R, Wong K, Zhang YJ, Britz GW, Wong STC. Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning. J Neurointerv Surg 2024; 16:290-295. [PMID: 37344174 DOI: 10.1136/jnis-2023-020300] [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: 03/07/2023] [Accepted: 04/20/2023] [Indexed: 06/23/2023]
Abstract
BACKGROUND Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. METHODS Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.
Collapse
Affiliation(s)
- Rahul Ghosh
- Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas, USA
- Biomedical Engineering, Texas A&M University System, College Station, Texas, USA
| | - Kelvin Wong
- Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas, USA
- Texas A&M University School of Medicine, Bryan, Texas, USA
| | | | - Gavin W Britz
- Neurological Surgery, Houston Methodist Hospital, Houston, Texas, USA
- Houston Methodist Neurological Institute, Houston, Texas, USA
| | - Stephen T C Wong
- Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas, USA
- Texas A&M University School of Medicine, Bryan, Texas, USA
| |
Collapse
|
7
|
Oliveira LC, Bonkhoff AK, Regenhardt RW, Alhadid K, Tuozzo C, Etherton MR, Rost NS, Schirmer MD. Neuroimaging markers of patient-reported outcome measures in acute ischemic stroke. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.27.23299829. [PMID: 38234738 PMCID: PMC10793527 DOI: 10.1101/2023.12.27.23299829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Objectives To determine the relationship between patient-reported outcome measures (PROMs) and volumetric imaging markers in acute ischemic stroke (AIS). Patients and Methods Patients presenting at Massachusetts General Hospital between February 14, 2017 and February 5, 2020 with a confirmed AIS by MRI were eligible and underwent a telephone interview including PROM-10 questionnaires 3-15 months after stroke. White matter hyperintensity (VWMH) and brain volumes (VBrain) were automatically determined using admission clinical MRI. Stroke lesions were manually segmented and volumes calculated (VLesion). Multivariable and ordinal regression analyses were performed to identify associations between global and PROM-10 subscores with brain volumetrics and clinical variables. Results Utilizing data from 167 patients (mean age: 64.7; 41.9% female), higher VWMH was associated with worse global physical (β=-0.6), global mental (β=-0.65), physical health (OR=0.68), social satisfaction (OR=0.66), fatigue (OR=0.69) and social activities (OR=0.59) scores. Higher VLesion was associated with poorer global mental (β=-0.79), mental health (OR=0.68), physical (OR=0.66) and social activities (OR=0.55), and emotional distress (OR=0.68) scores. Higher VBrain was linked to better global mental (β=0.93), global physical (β=0.79), mental health (OR=1.54) and physical activities (OR=1.72) scores. Conclusions Neuroimaging biomarkers were significantly associated with PROMs, where higher VWMH and VLesion led to worse outcome, while higher VBrain was protective. The inclusion of neuroimaging analyses and PROMs in routine assessment provides enhanced understanding of post-stroke outcomes.
Collapse
Affiliation(s)
- Lara C Oliveira
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Anna K Bonkhoff
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Robert W Regenhardt
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Kenda Alhadid
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Carissa Tuozzo
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Mark R Etherton
- Biogen Inc. Stroke/Acute Neurology Neurovascular Therapeutics Development Unit. Cambridge, MA. USA
| | - Natalia S Rost
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| | - Markus D Schirmer
- J Philip Kistler Stroke Research Center. Department of Neurology. Massachusetts General Hospital. Harvard Medical School, Boston, MA, United States of America
| |
Collapse
|
8
|
Johansen J, Offersen CM, Carlsen JF, Ingala S, Hansen AE, Nielsen MB, Darkner S, Pai A. An Automatic DWI/FLAIR Mismatch Assessment of Stroke Patients. Diagnostics (Basel) 2023; 14:69. [PMID: 38201378 PMCID: PMC10802848 DOI: 10.3390/diagnostics14010069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issues: binary classification of a non-binary problem and the subjectiveness of the assessor. In this article, we present a simple automatic method for segmenting stroke-related parenchymal hyperintensities on FLAIR, allowing for an automatic and continuous DWI/FLAIR mismatch assessment. We further show that our method's segmentations have comparable inter-rater agreement (DICE 0.820, SD 0.12) compared to that of two neuro-radiologists (DICE 0.856, SD 0.07), that our method appears robust to hyper-parameter choices (suggesting good generalizability), and lastly, that our methods continuous DWI/FLAIR mismatch assessment correlates to mismatch assessments made for a cohort of wake-up stroke patients at hospital submission. The proposed method shows promising results in automating the segmentation of parenchymal hyperintensity within ischemic stroke lesions and could help reduce inter-observer variability of DWI/FLAIR mismatch assessment performed in clinical environments as well as offer a continuous assessment instead of the current binary one.
Collapse
Affiliation(s)
- Jacob Johansen
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
- Cerebriu A/S, 1434 Copenhagen, Denmark;
| | - Cecilie Mørck Offersen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Silvia Ingala
- Cerebriu A/S, 1434 Copenhagen, Denmark;
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Michael Bachmann Nielsen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
- Cerebriu A/S, 1434 Copenhagen, Denmark;
| |
Collapse
|
9
|
Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13030532. [PMID: 36766637 PMCID: PMC9914778 DOI: 10.3390/diagnostics13030532] [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: 01/09/2023] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
Collapse
Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
- Correspondence:
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| |
Collapse
|
10
|
Zeng Y, Long C, Zhao W, Liu J. Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. J Clin Med 2022; 11:jcm11144008. [PMID: 35887776 PMCID: PMC9325315 DOI: 10.3390/jcm11144008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 02/01/2023] Open
Abstract
Purpose: To develop a preliminary deep learning model that uses diffusion-weighted imaging (DWI) images to classify the severity of neurological impairment caused by ischemic stroke. Materials and Methods: This retrospective study included 851 ischemic stroke patients (711 patients in the training set and 140 patients in the test set). The patients’ NIHSS scores, which reflect the severity of neurological impairment, were reviewed upon admission and on Day 7 of hospitalization and were classified into two stages (stage 1 for NIHSS < 5 and stage 2 for NIHSS ≥ 5). A 3D-CNN was trained to predict the stage of NIHSS based on different preprocessed DWI images. The performance in predicting the severity of anterior and posterior circulation stroke was also investigated. The AUC, specificity, and sensitivity were calculated to evaluate the performance of the model. Results: Our proposed model obtained better performance in predicting the NIHSS stage on Day 7 of hospitalization than that at admission (best AUC 0.895 vs. 0.846). Model D trained with DWI images (normalized with z-score and resized to 256 × 256 × 64 voxels) achieved the best AUC of 0.846 in predicting the NIHSS stage at admission. Model E rained with DWI images (normalized with maximum−minimum and resized to 128 × 128 × 32 voxels) achieved the best AUC of 0.895 in predicting the NIHSS stage on Day 7 of hospitalization. Our model also showed promising performance in predicting the NIHSS stage on Day 7 of hospitalization for anterior and posterior circulation stroke, with the best AUCs of 0.905 and 0.903, respectively. Conclusions: Our proposed 3D-CNN model can effectively predict the neurological severity of IS using DWI images and performs better in predicting the NIHSS stage on Day 7 of hospitalization. The model also obtained promising performance in subgroup analysis, which can potentially help clinical decision making.
Collapse
Affiliation(s)
- Ying Zeng
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Department of Radiology, Xiangtan Central Hospital, Xiangtan 411199, China
| | - Chen Long
- Department of Stroke Unit, Xiangtan Central Hospital, Xiangtan 411199, China;
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Department of Radiology Quality Control Center, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
| |
Collapse
|