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Zaragita N, Zhou S, Nugroho SW, Kaliaperumal C. Breaking boundaries in neurosurgery through art and technology: A historical perspective. BRAIN & SPINE 2024; 4:102836. [PMID: 38841149 PMCID: PMC11152706 DOI: 10.1016/j.bas.2024.102836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/11/2024] [Accepted: 05/17/2024] [Indexed: 06/07/2024]
Abstract
Introduction Since the past, art has been used as a tool to elaborate anatomical knowledge and guide surgeons to perform surgeries. Through the eras, art has taken role by conveying the knowledge to people in forms of illustrations and models, including neuroanatomy knowledge for neurosurgical purposes. With the advancement of technology, neurosurgical trainings and care evolve more than before. Research question How do art and technology play role in tbe education and development of neurosurgery? Materials and methods A literature search was conducted to find the role of art and technology in forms of illustrations, models, or others in neurosurgery. Results Illustration was known as one of the tools to understand it in the past. Now, in the modern era, neurosurgical learning, training, and teaching process have integrated both art and technology throughout the process. Not only as two-dimenional drawings, art and technology have gone as far as being developed into three-dimensional models and create specific models for surgical plannings and simulations. Artificial intelligence, virtual reality, and augmented reality have also been used to achieve accurate and efficient learning process and neurosurgical care. Discussion and conclusion Art does take significant role in the progression of neurosurgery. When combined with technology, art give greater utility and impact through the learning, teaching process, and delivery of care in neurosurgical world.
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Affiliation(s)
- Nadya Zaragita
- Department of Neurosurgery, Faculty of Medicine, Universitas Indonesia – Dr. Cipto Mangunkusumo General Hospital, Indonesia
| | - Stefano Zhou
- Faculty of Medicine, University of Edinburgh, UK
| | - Setyo Widi Nugroho
- Department of Neurosurgery, Faculty of Medicine, Universitas Indonesia – Dr. Cipto Mangunkusumo General Hospital, Indonesia
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El-Ghandour NMF. Commentary: From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery. Neurosurgery 2024; 94:e46-e47. [PMID: 37988063 DOI: 10.1227/neu.0000000000002764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 11/22/2023] Open
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Habibi MA, Fakhfouri A, Mirjani MS, Razavi A, Mortezaei A, Soleimani Y, Lotfi S, Arabi S, Heidaresfahani L, Sadeghi S, Minaee P, Eazi S, Rashidi F, Shafizadeh M, Majidi S. Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants. Neurosurg Rev 2024; 47:34. [PMID: 38183490 DOI: 10.1007/s10143-023-02271-2] [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: 11/15/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.
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Affiliation(s)
- Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
| | - Amirata Fakhfouri
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Mohammad Sina Mirjani
- Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Alireza Razavi
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ali Mortezaei
- Student Research Committee, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Yasna Soleimani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sohrab Lotfi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Shayan Arabi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Ladan Heidaresfahani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sara Sadeghi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Poriya Minaee
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - SeyedMohammad Eazi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Farhang Rashidi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Shafizadeh
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Shahram Majidi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
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Buyck F, Vandemeulebroucke J, Ceranka J, Van Gestel F, Cornelius JF, Duerinck J, Bruneau M. Computer-vision based analysis of the neurosurgical scene - A systematic review. BRAIN & SPINE 2023; 3:102706. [PMID: 38020988 PMCID: PMC10668095 DOI: 10.1016/j.bas.2023.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 12/01/2023]
Abstract
Introduction With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.
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Affiliation(s)
- Félix Buyck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Jakub Ceranka
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Frederick Van Gestel
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jan Frederick Cornelius
- Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Johnny Duerinck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Michaël Bruneau
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
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Watanabe G, Conching A, Nishioka S, Steed T, Matsunaga M, Lozanoff S, Noh T. Themes in neuronavigation research: A machine learning topic analysis. World Neurosurg X 2023; 18:100182. [PMID: 37013107 PMCID: PMC10066551 DOI: 10.1016/j.wnsx.2023.100182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/22/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Objective To understand trends in neuronavigation we employed machine learning methods to perform a broad literature review which would be impractical by manual inspection. Methods PubMed was queried for articles with "Neuronavigation" in any field from inception-2020. Articles were designated neuronavigation-focused (NF) if "Neuronavigation" was a major MeSH. The latent dirichlet allocation topic modeling technique was used to identify themes of NF research. Results There were 3896 articles of which 1727 (44%) were designated as NF. Between 1999-2009 and 2010-2020, the number of NF publications experienced 80% growth. Between 2009-2014 and 2015-2020, there was a 0.3% decline. Eleven themes covered 1367 (86%) NF articles. "Resection of Eloquent Lesions" comprised the highest number of articles (243), followed by "Accuracy and Registration" (242), "Patient Outcomes" (156), "Stimulation and Mapping" (126), "Planning and Visualization" (123), "Intraoperative Tools" (104), "Placement of Ventricular Catheters" (86), "Spine Surgery" (85), "New Systems" (80), "Guided Biopsies" (61), and "Surgical Approach" (61). All topics except for "Planning and Visualization", "Intraoperative Tools", and "New Systems" exhibited a monotonic positive trend. When analyzing subcategories, there were a greater number of clinical assessments or usage of existing neuronavigation systems (77%) rather than modification or development of new apparatuses (18%). Conclusion NF research appears to focus on the clinical assessment of neuronavigation and to a lesser extent on the development of new systems. Although neuronavigation has made significant strides, NF research output appears to have plateaued in the last decade.
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Ortiz AV, Feldman MJ, Yengo-Kahn AM, Roth SG, Dambrino RJ, Chitale RV, Chambless LB. Words matter: using natural language processing to predict neurosurgical residency match outcomes. J Neurosurg 2023; 138:559-566. [PMID: 35901704 DOI: 10.3171/2022.5.jns22558] [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: 03/11/2022] [Accepted: 05/10/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Narrative letters of recommendation (NLORs) are considered by neurosurgical program directors to be among the most important parts of the residency application. However, the utility of these NLORs in predicting match outcomes compared to objective measures has not been determined. In this study, the authors compare the performance of machine learning models trained on applicant NLORs and demographic data to predict match outcomes and investigate whether narrative language is predictive of standardized letter of recommendation (SLOR) rankings. METHODS This study analyzed 1498 NLORs from 391 applications submitted to a single neurosurgery residency program over the 2020-2021 cycle. Applicant demographics and match outcomes were extracted from Electronic Residency Application Service applications and training program websites. Logistic regression models using least absolute shrinkage and selection operator were trained to predict match outcomes using applicant NLOR text and demographics. Another model was trained on NLOR text to predict SLOR rankings. Model performance was estimated using area under the curve (AUC). RESULTS Both the NLOR and demographics models were able to discriminate similarly between match outcomes (AUCs 0.75 and 0.80; p = 0.13). Words including "outstanding," "seamlessly," and "AOA" (Alpha Omega Alpha) were predictive of match success. This model was able to predict SLORs ranked in the top 5%. Words including "highest," "outstanding," and "best" were predictive of the top 5% SLORs. CONCLUSIONS NLORs and demographic data similarly discriminate whether applicants will or will not match into a neurosurgical residency program. However, NLORs potentially provide further insight regarding applicant fit. Because words used in NLORs are predictive of both match outcomes and SLOR rankings, continuing to include narrative evaluations may be invaluable to the match process.
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Affiliation(s)
| | - Michael J Feldman
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Aaron M Yengo-Kahn
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Steven G Roth
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Robert J Dambrino
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rohan V Chitale
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lola B Chambless
- 2Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
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Danilov G, Kalayeva D, Vikhrova N, Konakova T, Zagorodnova A, Popova A, Postnov A, Shugay S, Pronin I. Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma. Sovrem Tekhnologii Med 2023; 15:5-11. [PMID: 37388754 PMCID: PMC10306961 DOI: 10.17691/stm2023.15.1.01] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Indexed: 12/10/2023] Open
Abstract
UNLABELLED Modern methodology of PET/CT quantitative analysis in patients with glioblastomas is not strictly standardized in clinic settings and does not exclude the influence of the human factor. Methods of radiomics may facilitate unification, and improve objectivity and efficiency of the medical image analysis. The aim of the study is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the 11С-methionine tumor-to-normal brain uptake ratio (TNR) determined by an expert in routine. MATERIALS AND METHODS PET/CT data (2018-2020) from 40 patients (average age was 55±12 years; 77.5% were males) with a histologically confirmed diagnosis of "glioblastoma" were included in the analysis. TNR was calculated as a ratio of the standardized uptake value of 11C-methionine measured in the tumor and intact tissue. Calculation of radiomic features for each PET was performed in the specified volumetric region of interest, capturing the tumor with the surrounding tissues. The relationship between TNR and the radiomic features was determined using the linear regression model. Predictors were included in the model following correlation analysis and LASSO regularization. The experiment with machine learning was repeated 300 times, splitting the training (70%) and test (30%) subsets randomly. The model quality metrics and predictor significance obtained in 300 tests were summarized. RESULTS Of 412 PET/CT radiomic parameters significantly correlated with TNR (p<0.05), the regularization procedure left no more than 30 in each model (the median number of predictors was 9 [7; 13]). The experiment has demonstrated a non-random linear correlation (the Spearman correlation coefficient was 0.58 [0.43; 0.74]) between TNR and separate radiomic features, primarily fractal dimensions, characterizing the geometrical properties of the image. CONCLUSION Radiomics enabled an objective determination of PET/CT image texture features reflecting the biological activity of glioblastomas. Despite the existing limitations in the application, the first results provide a good perspective of these methods in neurooncology.
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Affiliation(s)
- G.V. Danilov
- Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - D.B. Kalayeva
- Medical Physicist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; PhD Student; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia
| | - N.B. Vikhrova
- Radiologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - T.A. Konakova
- PhD Student; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - A.I. Zagorodnova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Popova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Postnov
- Researcher; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Associate Professor; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia; Researcher; Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninskiy Prospect, Moscow, 119991, Russia; Head of the Project; Research Institute of Technical Physics and Automation, 46 Varshavskoye Shosse, Moscow, 115230, Russia
| | - S.V. Shugay
- Professor, Academician of the Russian Academy of Sciences, Deputy Director for Research, Head of the Unit for X-ray and Radioisotopic Methods of Diagnosis; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - I.N. Pronin
- Pathologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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Iqbal J, Jahangir K, Mashkoor Y, Sultana N, Mehmood D, Ashraf M, Iqbal A, Hafeez MH. The future of artificial intelligence in neurosurgery: A narrative review. Surg Neurol Int 2022; 13:536. [DOI: 10.25259/sni_877_2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/19/2022] Open
Abstract
Background:
Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.
Methods:
A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.
Results:
The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.
Conclusion:
Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.
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Affiliation(s)
- Javed Iqbal
- School of Medicine, King Edward Medical University Lahore, Punjab, Pakistan,
| | - Kainat Jahangir
- School of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan,
| | - Yusra Mashkoor
- Department of Internal Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan,
| | - Nazia Sultana
- School of Medicine, Government Medical College, Siddipet, Telangana, India,
| | - Dalia Mehmood
- Department of Community Medicine, Fatima Jinnah Medical University, Lahore, Punjab, Pakistan,
| | - Mohammad Ashraf
- Wolfson School of Medicine, University of Glasgow, Scotland, United Kingdom,
| | - Ather Iqbal
- House Officer, Holy Family Hospital Rawalpindi, Punjab, Pakistan,
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Despujol I, Castañeda L, Marín VI, Turró C. What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review. INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 2022; 19:53. [PMID: 36259065 PMCID: PMC9560731 DOI: 10.1186/s41239-022-00359-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/06/2022] [Indexed: 06/16/2023]
Abstract
By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators.
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Affiliation(s)
| | | | | | - Carlos Turró
- Universitat Politècnica de València, Valencia, Spain
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Rashid J, Kim J, Hussain A, Naseem U, Juneja S. A novel multiple kernel fuzzy topic modeling technique for biomedical data. BMC Bioinformatics 2022; 23:275. [PMID: 35820793 PMCID: PMC9277941 DOI: 10.1186/s12859-022-04780-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. METHODS In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. RESULTS Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. CONCLUSION We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.
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Affiliation(s)
- Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, Korea
| | - Jungeun Kim
- Department of Software, Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, Korea.
| | - Amir Hussain
- Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh, EH11 4DY, UK
| | - Usman Naseem
- School of Computer Science, University of Sydney, Sydney, Australia
| | - Sapna Juneja
- Department of Computer Science, KIET Group of Institutions, Dehli NCR, Ghaziabad, India
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Shevchenko EV, Danilov GV, Usachev DY, Lukshin VA, Kotik KV, Ishankulov TA. [Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2022; 86:43-51. [PMID: 36534623 DOI: 10.17116/neiro20228606143] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach. OBJECTIVE To evaluate the quality of AI-guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records in comparison with expectations of patients and physicians. MATERIAL AND METHODS AI-guided prediction was based on analysis of unstructured text records of the electronic medical history (preoperative examination and surgical protocol). Predictive models were learned on the data of the Burdenko Neurosurgery Center accumulated for the period 2000-2017 (90.688 cases). Model testing was performed on 111 completed neurosurgical cases in a prospective study. We compared the accuracy of prediction models compared to expectations of patients and physicians regarding hospital-stay. RESULTS The median absolute error of machine prediction in the final test was 2.00 days. This value was comparable with the doctor's prediction error. CONCLUSION This study demonstrated the possibility of using unstructured textual data to predict the length of hospital-stay in a neurosurgical hospital.
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Affiliation(s)
| | - G V Danilov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | - V A Lukshin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - K V Kotik
- Burdenko Neurosurgical Center, Moscow, Russia
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Danilov GV, Ishankulov TA, Kotik KV, Shifrin MA, Potapov AA. [Artificial intelligence technologies in clinical neurooncology]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2022; 86:127-133. [PMID: 36534634 DOI: 10.17116/neiro202286061127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.
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Affiliation(s)
- G V Danilov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | - K V Kotik
- Burdenko Neurosurgical Center, Moscow, Russia
| | - M A Shifrin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A A Potapov
- Burdenko Neurosurgical Center, Moscow, Russia
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Byvaltsev VА, Kalinin АА. Assessment of Clinical Decision Support System Efficiency in Spinal Neurosurgery for Personalized Minimally Invasive Technologies Used on Lumbar Spine. Sovrem Tekhnologii Med 2021; 13:13-21. [PMID: 35265345 PMCID: PMC8858415 DOI: 10.17691/stm2021.13.5.02] [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: 03/17/2021] [Indexed: 11/14/2022] Open
Abstract
The aim of the study was to assess clinical decision support system (CDSS) in spinal surgery for personalized minimally invasive technologies on lumbar spine. Materials and Methods The prospective study involved 59 patients operated on using CDSS based on a personalized surgical algorithm considering patient-specific parameters of lumbar segments. Among them, 11 patients underwent total disk replacement (TDR), 25 and 23 patients had minimally invasive (MI-TLIF) and open (O-TLIF) dorsal rigid stabilization, respectively, according to an original technology. The comparative analysis was carried out using retrospective findings of 196 patients operated on involving TDR (n=42), MI-TLIF (n=79), and O-TLIF (n=75). The efficiency of CDSS medical algorithms was assessed by pain syndrome in the lumbar spine and lower limbs, as well as by patients' functional status on discharge according to ODI, 3 and 6 months after the operation. Results The comparison by gender characteristics and anthropometric data revealed no significant intergroup differences among the groups under study (p>0.05). Intergroup analysis of functional status by ODI, pain intensity in lower limbs and lumbar spine showed better clinical outcomes in patients operated using CDSS compared to a retrospective group (p<0.05): 6 months after TDR and O-TLIF, and 3 months after MI-TLIF. Conclusion The study findings demonstrated high efficiency of CDSS developed for personalized surgical treatment of patients with degenerative lumbar spine diseases taking into consideration individual biometric parameters of lumbar segments.
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Affiliation(s)
- V А Byvaltsev
- Professor, Head of the Department of Neurosurgery and Innovative Medicine Irkutsk State Medical University, 1 Krasnogo Vosstaniya St., Irkutsk, 664003, Russia;; Chief of Neurosurgery Center Road Clinical Hospital, 10 Botkin St., Irkutsk, 664005, Russia;; Professor, Department of Traumatology, Orthopedics and Neurosurgery Irkutsk State Medical Academy for Postgraduate Education, 100 Yubileyny Microdistrict, Irkutsk, 664049, Russia
| | - А А Kalinin
- Associate Professor, Department of Neurosurgery and Innovative Medicine Irkutsk State Medical University, 1 Krasnogo Vosstaniya St., Irkutsk, 664003, Russia;; Neurosurgeon, Neurosurgery Center Road Clinical Hospital, 10 Botkin St., Irkutsk, 664005, Russia
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