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Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [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: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
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2
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Williams KS. Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics. J Neurophysiol 2024; 131:825-831. [PMID: 38533950 DOI: 10.1152/jn.00404.2023] [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: 10/31/2023] [Revised: 03/05/2024] [Accepted: 03/24/2024] [Indexed: 03/28/2024] Open
Abstract
This article evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI/machine learning (ML) algorithms are analyzed, as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated, as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility, as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.
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3
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Mora S, Turrisi R, Chiarella L, Consales A, Tassi L, Mai R, Nobili L, Barla A, Arnulfo G. NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy. Sci Rep 2024; 14:2349. [PMID: 38287042 PMCID: PMC10825198 DOI: 10.1038/s41598-024-51846-6] [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] [Received: 08/30/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024] Open
Abstract
Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.
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Affiliation(s)
- Sara Mora
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy.
| | - Rosanna Turrisi
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Lorenzo Chiarella
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Alessandro Consales
- Division of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147, Genoa, Italy
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Roberto Mai
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Lino Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Annalisa Barla
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- Neuroscience Center, Helsinki Institute of Life Science (HiLife), University of Helsinki, 00014, Helsinki, Finland
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4
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Fernandes M, Cardall A, Jing J, Ge W, Moura LMVR, Jacobs C, McGraw C, Zafar SF, Westover MB. Identification of patients with epilepsy using automated electronic health records phenotyping. Epilepsia 2023; 64:1472-1481. [PMID: 36934317 PMCID: PMC10239346 DOI: 10.1111/epi.17589] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/20/2023]
Abstract
OBJECTIVE Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aidan Cardall
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lidia M. V. R. Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Claire Jacobs
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher McGraw
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
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5
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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6
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Varotto G, Susi G, Tassi L, Gozzo F, Franceschetti S, Panzica F. Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy. Front Neuroinform 2021; 15:715421. [PMID: 34867255 PMCID: PMC8641296 DOI: 10.3389/fninf.2021.715421] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/23/2021] [Indexed: 11/30/2022] Open
Abstract
Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery. Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered. Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method. Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.
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Affiliation(s)
- Giulia Varotto
- Epilepsy Unit, Bioengineering Group, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gianluca Susi
- Universidad Complutense de Madrid-Universidad Politécnica de Madrid (UPM-UCM) Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Logopedy, Complutense University of Madrid, Madrid, Spain
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - Francesca Gozzo
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - Silvana Franceschetti
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ferruccio Panzica
- Clinical Engineering, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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7
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Ibrahim O, Sutherland HG, Lea RA, Nasrallah F, Maksemous N, Smith RA, Haupt LM, Griffiths LR. Discriminating head trauma outcomes using machine learning and genomics. J Mol Med (Berl) 2021; 100:303-312. [PMID: 34797388 DOI: 10.1007/s00109-021-02158-z] [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: 04/29/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
A percentage of the population suffers prolonged and persistent post-concussion symptoms (PCS) following average head injuries or develops severe neurological dysfunction following minor head trauma. Genetic variants that may contribute to individual response to head trauma have been investigated in some studies, but to date none have explored the use of machine learning (ML) methods with genomic data to specifically explore outcomes of head trauma. Whole exome sequencing (WES) was completed for three groups of individuals (N = 60): (a) 16 individuals with severe neurological responses to minor head trauma, (b) 26 individuals with persistent PCS and (c) 18 individuals with normal recovery from concussion or mTBI. Gradient boosted tree algorithms were applied to the data using XGBoost. By using variants with CADD scores above 15 in the training set (randomly sampled 70%), we identified signatures that accurately distinguish to accurately distinguish the test groups with an average area under the curve (AUC) of 0.8 (SE = 0.019). Metrics including positive and negative prediction values, as well as kappa were all within acceptable range to support the prediction accuracy. This study illustrates how ML methods in combination with WES data have the potential to predict severe or prolonged responses to head trauma from healthy recovery. KEY MESSAGES: Linear association analysis has been inconclusive in concussion genetics. Non-linear methods as boosted trees can offer better insights in small samples. Strong discrimination trends can be achieved from exome data of cases and controls.
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Affiliation(s)
- Omar Ibrahim
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Heidi G Sutherland
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Rodney A Lea
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Fatima Nasrallah
- The Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Neven Maksemous
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Robert A Smith
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Larisa M Haupt
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Lyn R Griffiths
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia.
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8
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Abstract
Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.
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Affiliation(s)
- Mohammad Mofatteh
- Sir William Dunn School of Pathology, Medical Sciences Division, University of Oxford, South Parks Road, Oxford OX1 3RE, United Kingdom
- Lincoln College, University of Oxford, Turl Street, Oxford OX1 3DR, United Kingdom
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9
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Classifying epilepsy pragmatically: Past, present, and future. J Neurol Sci 2021; 427:117515. [PMID: 34174531 PMCID: PMC7613525 DOI: 10.1016/j.jns.2021.117515] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 01/31/2023]
Abstract
The classification of epilepsy is essential for people with epilepsy and their families, healthcare providers, physicians and researchers. The International League Against Epilepsy proposed updated seizure and epilepsy classifications in 2017, while another four-dimensional epilepsy classification was updated in 2019. An Integrated Epilepsy Classification system was proposed in 2020. Existing classifications, however, lack consideration of important pragmatic factors relevant to the day-to-day life of people with epilepsy and stakeholders. Despite promising developments, consideration of comorbidities in brain development, genetic causes, and environmental triggers of epilepsy remains largely user-dependent in existing classifications. Demographics of epilepsy have changed over time, while existing classification schemes exhibit caveats. A pragmatic classification scheme should incorporate these factors to provide a nuanced classification. Validation across disparate contexts will ensure widespread applicability and ease of use. A team-based approach may simplify communication between healthcare personnel, while an individual-centred perspective may empower people with epilepsy. Together, incorporating these elements into a modern but pragmatic classification scheme may ensure optimal care for people with epilepsy by emphasising cohesiveness among its myriad users. Technological advancements such as 7T MRI, next-generation sequencing, and artificial intelligence may affect future classification efforts.
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10
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Lewis-Smith D, Galer PD, Balagura G, Kearney H, Ganesan S, Cosico M, O'Brien M, Vaidiswaran P, Krause R, Ellis CA, Thomas RH, Robinson PN, Helbig I. Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable. Epilepsia 2021; 62:1293-1305. [PMID: 33949685 PMCID: PMC8272408 DOI: 10.1111/epi.16908] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/19/2021] [Accepted: 04/01/2021] [Indexed: 01/08/2023]
Abstract
Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. Methods: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort’s data were provided in a different format and harmonized using the two versions of the HPO. Results: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals’ seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures.
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Affiliation(s)
- David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ganna Balagura
- Medical Genetics Unit, IRCSS Giannina Gaslini Institute, Genoa, Italy
| | - Hugh Kearney
- FutureNeuro the SFI Research Centre for Chronic and Rare Neurological Diseases, Royal College of Surgeons in Ireland, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mahgenn Cosico
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret O'Brien
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Priya Vaidiswaran
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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11
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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12
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Panesar SS, Kliot M, Parrish R, Fernandez-Miranda J, Cagle Y, Britz GW. Promises and Perils of Artificial Intelligence in Neurosurgery. Neurosurgery 2020; 87:33-44. [PMID: 31748800 DOI: 10.1093/neuros/nyz471] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/28/2019] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.
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Affiliation(s)
- Sandip S Panesar
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
| | - Michel Kliot
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Rob Parrish
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
| | | | - Yvonne Cagle
- NASA Ames Research Center, Mountain View, California
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas
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13
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Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning. Proc Inst Mech Eng H 2020; 234:1051-1069. [DOI: 10.1177/0954411920938567] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.
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14
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An S, Kang C, Lee HW. Artificial Intelligence and Computational Approaches for Epilepsy. J Epilepsy Res 2020; 10:8-17. [PMID: 32983950 PMCID: PMC7494883 DOI: 10.14581/jer.20003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/18/2020] [Accepted: 07/14/2020] [Indexed: 12/30/2022] Open
Abstract
Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.
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Affiliation(s)
- Sora An
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Chaewon Kang
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
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15
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Chiang KL, Huang CY, Hsieh LP, Chang KP. A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome. Epilepsy Behav 2020; 106:107021. [PMID: 32224446 DOI: 10.1016/j.yebeh.2020.107021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 03/02/2020] [Accepted: 03/02/2020] [Indexed: 01/01/2023]
Abstract
PURPOSE The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. METHODS We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. RESULTS Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis. CONCLUSION We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.
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Affiliation(s)
- Kuo-Liang Chiang
- Department of Pediatric Neurology, Kuang-Tien General Hospital, No. 117, Shatian Road, Shalu District, Taichung 43303, Taiwan; Department of Nutrition, Hungkuang University, No. 1018, Section 6, Taiwan Boulevard, Shalu District, Taichung 43302, Taiwan; Department of Industrial Engineering and Enterprise Information, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan.
| | - Chin-Yin Huang
- Department of Industrial Engineering and Enterprise Information, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan; Program for Health Administration, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan.
| | - Liang-Po Hsieh
- Department of Neurology, Cheng-Ching Hospital, No. 966, Section 4, Taiwan Boulevard, Xitun District, Taichung 40764, Taiwan
| | - Kai-Ping Chang
- Department of Pediatric Neurology, Taipei Veterans General Hospital, No.201, Section 2, Shipai Rd., Beitou District, Taipei 11217, Taiwan
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16
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Liu T, Truong ND, Nikpour A, Zhou L, Kavehei O. Epileptic Seizure Classification With Symmetric and Hybrid Bilinear Models. IEEE J Biomed Health Inform 2020; 24:2844-2851. [PMID: 32248133 DOI: 10.1109/jbhi.2020.2984128] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Epilepsy affects nearly [Formula: see text] of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly specialized and labour-intensive. The accuracy of the diagnosis is also complicated by overlapping medical symptoms, varying levels of experience and inter-observer variability among clinical professions. This paper proposes a novel hybrid bilinear deep learning network with an application in the clinical procedures of epilepsy classification diagnosis, where the use of surface electroencephalogram (sEEG) and audiovisual monitoring is standard practice. Hybrid bilinear models based on two types of feature extractors, namely Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained using Short-Time Fourier Transform (STFT) of one-second sEEG. In the proposed hybrid models, CNNs extract spatio-temporal patterns, while RNNs focus on the characteristics of temporal dynamics in relatively longer intervals given the same input data. Second-order features, based on interactions between these spatio-temporal features are further explored by bilinear pooling and used for epilepsy classification. Our proposed methods obtain an F1-score of [Formula: see text] on the Temple University Hospital Seizure Corpus and [Formula: see text] on the EPILEPSIAE dataset, comparing favourably to existing benchmarks for sEEG-based seizure type classification. The open-source implementation of this study is available at https://github.com/NeuroSyd/Epileptic-Seizure-Classification.
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17
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Hosseini MP, Tran TX, Pompili D, Elisevich K, Soltanian-Zadeh H. Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing. Artif Intell Med 2020; 104:101813. [DOI: 10.1016/j.artmed.2020.101813] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 12/26/2019] [Accepted: 01/31/2020] [Indexed: 11/28/2022]
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18
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Zhou B, An D, Xiao F, Niu R, Li W, Li W, Tong X, Kemp GJ, Zhou D, Gong Q, Lei D. Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging. Front Med 2020; 14:630-641. [PMID: 31912429 DOI: 10.1007/s11684-019-0718-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 09/07/2019] [Indexed: 02/04/2023]
Abstract
Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.
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Affiliation(s)
- Baiwan Zhou
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Dongmei An
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Fenglai Xiao
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China.,Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, WC1E 6BT, UK
| | - Running Niu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Wei Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xin Tong
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Graham J Kemp
- Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, L9 7AL, UK
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China.,Department of Psychology, School of Public Administration, Sichuan University, Chengdu, 610041, China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China. .,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK. .,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA.
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19
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Li Y, Tan Z, Wang Y, Wang Y, Li D, Chen Q, Huang W. Detection of differentiated changes in gray matter in children with progressive hydrocephalus and chronic compensated hydrocephalus using voxel-based morphometry and machine learning. Anat Rec (Hoboken) 2019; 303:2235-2247. [PMID: 31654555 DOI: 10.1002/ar.24306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 08/31/2019] [Accepted: 09/22/2019] [Indexed: 12/22/2022]
Abstract
Currently, no neuroimaging study has reported the detection of specific imaging biomarkers that distinguish the progressive hydrocephalus (PH) and chronic compensated hydrocephalus (CH). Our main focus is to evaluate the different structural changes in classifying the two types of hydrocephalus children. Twenty-two children with hydrocephalus (12 PHs and 10 CHs) and 30 age-matched healthy controls were enrolled and the T1-weighted imaging was collected in the study. A customized voxel-based morphometry (VBM) approach and support vector machine (SVM) were combined to investigate the structural changes and group classification. Comparing with the controls and CH, PH groups invariably showed a significant decrease of GM volume in the bilateral hippocampus/parahippocampus, insula, and motor-related areas. SVM applied to the GM volumes of bilateral hippocampus/parahippocampus, insula, and motor-related areas correctly identified hydrocephalus children from normal controls with a statistically significant accuracy of 88.46% (p ≤ .001). In addition, SVM applied to GM volumes of the same regions correctly identified PH from CH with a statistically significant accuracy of 77.27% (p ≤ .009). Using VBM analysis, we characterized and visualized the GM changes in children with hydrocephalus. Machine learning results further confirmed that a significant decrease of the bilateral hippocampus/parahippocampus, insula, and motor-related GM volume can serve as a specific neuroimaging index to distinguish the children with PH from the children with CH and controls at individual. The findings could help to aid the identification of individuals with PH in clinical practice.
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Affiliation(s)
- Yongxin Li
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Zhen Tan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yanfang Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ding Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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20
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Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
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21
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Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, Smith TR. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review. Neurosurgery 2019; 83:181-192. [PMID: 28945910 DOI: 10.1093/neuros/nyx384] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 08/11/2017] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." METHODS A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. RESULTS Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. CONCLUSION We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.
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Affiliation(s)
- Joeky T Senders
- Department of Neurosurgery, University Medical Center, Utrecht, the Netherlands.,Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Omar Arnaout
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Neurological Surgery, Northwestern University School of Medicine, Chicago, Illinois
| | - Aditya V Karhade
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Hormuzdiyar H Dasenbrock
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - William B Gormley
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marike L Broekman
- Department of Neurosurgery, University Medical Center, Utrecht, the Netherlands.,Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Timothy R Smith
- Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Wang J, Li Y, Wang Y, Huang W. Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures. Front Neurol 2018; 9:1038. [PMID: 30619025 PMCID: PMC6297879 DOI: 10.3389/fneur.2018.01038] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 01/16/2023] Open
Abstract
Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.
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Affiliation(s)
- Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongxin Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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Nakawala H, Ferrigno G, De Momi E. Development of an intelligent surgical training system for Thoracentesis. Artif Intell Med 2018; 84:50-63. [DOI: 10.1016/j.artmed.2017.10.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 06/19/2017] [Accepted: 10/31/2017] [Indexed: 11/24/2022]
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24
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Random ensemble learning for EEG classification. Artif Intell Med 2018; 84:146-158. [DOI: 10.1016/j.artmed.2017.12.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 01/21/2023]
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25
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Ahmedt-Aristizabal D, Fookes C, Dionisio S, Nguyen K, Cunha JPS, Sridharan S. Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey. Epilepsia 2017; 58:1817-1831. [DOI: 10.1111/epi.13907] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2017] [Indexed: 11/28/2022]
Affiliation(s)
- David Ahmedt-Aristizabal
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Clinton Fookes
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Sasha Dionisio
- Mater Centre for Neurosciences; Brisbane Queensland Australia
| | - Kien Nguyen
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - João Paulo S. Cunha
- The Institute of Systems and Computer Engineering; Technology and Science; and Faculty of Engineering; University of Porto; Porto Portugal
| | - Sridha Sridharan
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
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26
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Min H, Mobahi H, Irvin K, Avramovic S, Wojtusiak J. Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. J Biomed Semantics 2017; 8:39. [PMID: 28915930 PMCID: PMC5603095 DOI: 10.1186/s13326-017-0149-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Accepted: 09/06/2017] [Indexed: 01/29/2023] Open
Abstract
Background Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to “understand” biomedical data. Results This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient’s cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient’s race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies. Conclusions This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.
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Affiliation(s)
- Hua Min
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA.
| | - Hedyeh Mobahi
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA
| | - Katherine Irvin
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA
| | - Sanja Avramovic
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA
| | - Janusz Wojtusiak
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA
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Nakawala H, Ferrigno G, De Momi E. Toward a Knowledge-Driven Context-Aware System for Surgical Assistance. ACTA ACUST UNITED AC 2017. [DOI: 10.1142/s2424905x17400074] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Complex surgeries complications are increasing, thus making an efficient surgical assistance is a real need. In this work, an ontology-based context-aware system was developed for surgical training/assistance during Thoracentesis by using image processing and semantic technologies. We evaluated the Thoracentesis ontology and implemented a paradigmatic test scenario to check the efficacy of the system by recognizing contextual information, e.g. the presence of surgical instruments on the table. The framework was able to retrieve contextual information about current surgical activity along with information on the need or presence of a surgical instrument.
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Affiliation(s)
- Hirenkumar Nakawala
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, Milan 20133, Italy
| | - Giancarlo Ferrigno
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, Milan 20133, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, Milan 20133, Italy
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Nessi F, Beretta E, Gatti C, Ferrigno G, De Momi E. Gesteme-free context-aware adaptation of robot behavior in human–robot cooperation. Artif Intell Med 2016; 74:32-43. [DOI: 10.1016/j.artmed.2016.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 10/22/2016] [Accepted: 10/25/2016] [Indexed: 11/26/2022]
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Harnessing ontology and machine learning for RSO classification. SPRINGERPLUS 2016; 5:1655. [PMID: 27730017 PMCID: PMC5037103 DOI: 10.1186/s40064-016-3258-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 09/08/2016] [Indexed: 11/10/2022]
Abstract
Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated. Experiments on WEKA show that ontology-based classification gains a relatively high accuracy and precision for classifying RSOs. When classifying RSOs with imperfect data, ontology-based classification keeps its performances, showing evident advantages over classical machine learning classifiers who either have increases of 5 % at least in FP rate or have decreases of 5 % at least in indexes such as accuracy, precision and recall.
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Berges I, Antón D, Bermúdez J, Goñi A, Illarramendi A. TrhOnt: building an ontology to assist rehabilitation processes. J Biomed Semantics 2016; 7:60. [PMID: 27716359 PMCID: PMC5050577 DOI: 10.1186/s13326-016-0104-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 09/20/2016] [Indexed: 11/21/2022] Open
Abstract
Background One of the current research efforts in the area of biomedicine is the representation of knowledge in a structured way so that reasoning can be performed on it. More precisely, in the field of physiotherapy, information such as the physiotherapy record of a patient or treatment protocols for specific disorders must be adequately modeled, because they play a relevant role in the management of the evolutionary recovery process of a patient. In this scenario, we introduce TrhOnt, an application ontology that can assist physiotherapists in the management of the patients’ evolution via reasoning supported by semantic technology. Methods The ontology was developed following the NeOn Methodology. It integrates knowledge from ontological (e.g. FMA ontology) and non-ontological resources (e.g. a database of movements, exercises and treatment protocols) as well as additional physiotherapy-related knowledge. Results We demonstrate how the ontology fulfills the purpose of providing a reference model for the representation of the physiotherapy-related information that is needed for the whole physiotherapy treatment of patients, since they step for the first time into the physiotherapist’s office, until they are discharged. More specifically, we present the results for each of the intended uses of the ontology listed in the document that specifies its requirements, and show how TrhOnt can answer the competency questions defined within that document. Moreover, we detail the main steps of the process followed to build the TrhOnt ontology in order to facilitate its reproducibility in a similar context. Finally, we show an evaluation of the ontology from different perspectives. Conclusions TrhOnt has achieved the purpose of allowing for a reasoning process that changes over time according to the patient’s state and performance.
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Affiliation(s)
- Idoia Berges
- University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, Donostia-San Sebastián, 20018, Spain.
| | - David Antón
- University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, Donostia-San Sebastián, 20018, Spain
| | - Jesús Bermúdez
- University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, Donostia-San Sebastián, 20018, Spain
| | - Alfredo Goñi
- University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, Donostia-San Sebastián, 20018, Spain
| | - Arantza Illarramendi
- University of the Basque Country, UPV/EHU, Paseo Manuel de Lardizabal, 1, Donostia-San Sebastián, 20018, Spain
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31
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Diciolla M, Binetti G, Di Noia T, Pesce F, Schena FP, Vågane AM, Bjørneklett R, Suzuki H, Tomino Y, Naso D. Patient classification and outcome prediction in IgA nephropathy. Comput Biol Med 2015; 66:278-86. [PMID: 26453758 DOI: 10.1016/j.compbiomed.2015.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 08/08/2015] [Accepted: 09/02/2015] [Indexed: 10/23/2022]
Abstract
OBJECTIVE IgA Nephropathy (IgAN) is a common kidney disease which may entail renal failure, known as End Stage Kidney Disease (ESKD). One of the major difficulties dealing with this disease is to predict the time of the long-term prognosis for a patient at the time of diagnosis. In fact, the progression of IgAN to ESKD depends on an intricate interrelationship between clinical and laboratory findings. Therefore, the objective of this work has been the selection of the best data mining tool to build a model able to predict (I) if a patient with a biopsy proven IgAN will reach ESKD and (II) if a patient will reach the ESKD before or after 5 years. MATERIAL AND METHODS The largest available cohort study worldwide on IgAN has been used to design and compare several data-driven models. The complete dataset was composed of 1174 records collected from Italian, Norwegian, and Japanese IgAN patients, in the last 30 years. The data mining tools considered in this work were artificial neural networks (ANNs), neuro fuzzy systems (NFSs), support vector machines (SVMs), and decision trees (DTs). A 10-fold cross validation was used to evaluate unbiased performances for all the models. RESULTS An extensive model comparison based on accuracy, precision, recall, and f-measure was provided. Overall, the results indicate that ANNs can provide superior performance compared to the other models. The ANN for time-to-ESKD prediction is characterized by accuracy, precision, recall, and f-measure greater than 90%. The ANN for ESKD prediction has accuracy greater than 90% as well as precision, recall, and f-measure for the class of patients not reaching ESKD, while precision, recall, and f-measure for the class of patients reaching ESKD are slightly lower. The obtained model has been implemented in a Web-based decision support system (DSS). CONCLUSIONS The extraction of novel knowledge from clinical data and the definition of predictive models to support diagnosis, prognosis, and therapy is becoming an essential tool for researchers and clinical practitioners in medicine. The proposed comparative study of several data mining models for the outcome prediction in IgAN patients, using a large dataset of clinical records from three different countries, provides an insight into the relative prediction ability of the considered methods applied to such a disease.
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Affiliation(s)
- M Diciolla
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - G Binetti
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - T Di Noia
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
| | - F Pesce
- Cardiovascular Genetics and Genomics, National Heart & Lung Institute, Royal Brompton Hospital, Imperial College London, UK; Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - F P Schena
- Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy; C.A.R.S.O. Consortium, Valenzano-Casamassima, Italy
| | - A M Vågane
- Department of Clinical Medicine, Renal Research Group, University of Bergen, Bergen, Norway; Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - R Bjørneklett
- Department of Clinical Medicine, Renal Research Group, University of Bergen, Bergen, Norway; Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - H Suzuki
- Division of Nephrology, Department of Internal Medicine, Juntendo University, Faculty of Medicine, Tokyo, Japan
| | - Y Tomino
- Division of Nephrology, Department of Internal Medicine, Juntendo University, Faculty of Medicine, Tokyo, Japan
| | - D Naso
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
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