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Shlobin NA, Ward M, Shah HA, Brown EDL, Sciubba DM, Langer D, D'Amico RS. Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pretrained Transformer Chatbot-Based, Human-Modified Approach. World Neurosurg 2024:S1878-8750(24)00738-1. [PMID: 38723944 DOI: 10.1016/j.wneu.2024.04.165] [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: 03/04/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/31/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery. METHODS GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes. RESULTS Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed. CONCLUSIONS It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.
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Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
| | - Max Ward
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
| | - Harshal A Shah
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
| | - Ethan D L Brown
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
| | - Daniel M Sciubba
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
| | - David Langer
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
| | - Randy S D'Amico
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
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Sahin MC, Sozer A, Kuzucu P, Turkmen T, Sahin MB, Sozer E, Tufek OY, Nernekli K, Emmez H, Celtikci E. Beyond human in neurosurgical exams: ChatGPT's success in the Turkish neurosurgical society proficiency board exams. Comput Biol Med 2024; 169:107807. [PMID: 38091727 DOI: 10.1016/j.compbiomed.2023.107807] [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: 09/19/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 02/08/2024]
Abstract
Chat Generative Pre-Trained Transformer (ChatGPT) is a sophisticated natural language model that employs advanced deep learning techniques and is trained on extensive datasets to produce responses akin to human conversation for user inputs. In this study, ChatGPT's success in the Turkish Neurosurgical Society Proficiency Board Exams (TNSPBE) is compared to the actual candidates who took the exam, along with identifying the types of questions it answered incorrectly, assessing the quality of its responses, and evaluating its performance based on the difficulty level of the questions. Scores of all 260 candidates were recalculated according to the exams they took and included questions in those exams for ranking purposes of this study. The average score of the candidates for a total of 523 questions is 62.02 ± 0.61 compared to ChatGPT, which was 78.77. We have concluded that in addition to ChatGPT's higher response rate, there was also a correlation with the increase in clarity regardless of the difficulty level of the questions with Clarity 1.5, 2.0, 2.5, and 3.0. In the participants, however, there is no such increase in parallel with the increase in clarity.
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Affiliation(s)
- Mustafa Caglar Sahin
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey.
| | - Alperen Sozer
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey.
| | - Pelin Kuzucu
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey.
| | - Tolga Turkmen
- Ministry of Health Dortyol State Hospital, Department of Neurosurgery, Hatay, Turkey.
| | - Merve Buke Sahin
- Ministry of Health Etimesgut District Health Directorate, Department of Public Health, Ankara, Turkey.
| | - Ekin Sozer
- Gazi University, Directorate of Health Culture and Sports, Ankara, Turkey.
| | - Ozan Yavuz Tufek
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey.
| | - Kerem Nernekli
- Stanford University Medical School, Department of Radiology, Stanford, CA, USA.
| | - Hakan Emmez
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey.
| | - Emrah Celtikci
- Gazi University Faculty of Medicine, Department of Neurosurgery, Ankara, Turkey; Gazi University Artificial Intelligence Center, Ankara, Turkey.
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Amin A, Cardoso SA, Suyambu J, Abdus Saboor H, Cardoso RP, Husnain A, Isaac NV, Backing H, Mehmood D, Mehmood M, Maslamani ANJ. Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus 2024; 16:e51631. [PMID: 38318552 PMCID: PMC10839429 DOI: 10.7759/cureus.51631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.
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Affiliation(s)
- Aamir Amin
- Cardiothoracic Surgery, Harefield Hospital, Guy's and St Thomas' NHS Foundation Trust, London, GBR
| | - Swizel Ann Cardoso
- Major Trauma Services, University Hospital Birmingham NHS Foundation Trust DC, Birmingham, GBR
| | - Jenisha Suyambu
- Medicine, University of Perpetual Help System Data - Jonelta Foundation School of Medicine, Las Piñas, PHL
| | | | - Rayner P Cardoso
- Medicine and Surgery, All India Institute of Medical Sciences, Jodhpur, Jodhpur, IND
| | - Ali Husnain
- Radiology, Northwestern University, Lahore, PAK
| | - Natasha Varghese Isaac
- Medicine and Surgery, St John's Medical College Hospital, Rajiv Gandhi University of Health Sciences, Bengaluru, IND
| | - Haydee Backing
- Medicine, Universidad de San Martin de Porres, Lima, PER
| | - Dalia Mehmood
- Community Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Maria Mehmood
- Internal Medicine, Shalamar Medical and Dental College, Lahore, PAK
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Ruparelia J, Manjunath N, Nachiappan DS, Raheja A, Suri A. Virtual Reality in Preoperative Planning of Complex Cranial Surgery. World Neurosurg 2023; 180:e11-e18. [PMID: 37307986 DOI: 10.1016/j.wneu.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Changing paradigms of neurosurgical training and limited operative exposure during the residency period have made it necessary to evaluate newer technologies for training. Virtual reality (VR) technology provides three-dimensional reconstruction of routine imaging, along with the ability to see as well as interact. The application of VR technology in operative planning, which is an important part of neurosurgical training, has been incompletely studied so far. METHODS Sixteen final-year residents, post-M.Ch. (magister chirurgiae) residents, and fellows were included as study participants. They were divided into 2 groups based on their seniority for further analysis. Five complex cranial cases were selected and a multiple-choice question-based test was prepared by the authors, with 5 questions for each of the cases. The pretest score was determined based on performance on the test after participants accessed routine preoperative imaging. The posttest score was calculated after use of the VR system (ImmersiveTouch VR System, ImmersiveTouch Inc.). Analysis was performed by the investigators, who were blinded to the identity of the participant. Subanalysis based on the type of case and type of question was performed. Feedback was obtained from each participant regarding VR use. RESULTS There was an overall improvement in scores from pretest to posttest, which was also noted in the analysis based on the participants' seniority. This improvement was noted to be more for the vascular cases (15.89%) compared with the tumor cases (7.84%). Participants also fared better in questions related to surgical anatomy and surgical approach, compared with questions based on the diagnosis. There was overall positive feedback from participants regarding VR use, and most participants wanted VR to become a routine part of operative planning. CONCLUSIONS Our study shows that there is improvement in understanding of surgical aspects after use of this VR system.
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Affiliation(s)
- Jigish Ruparelia
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Niveditha Manjunath
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | | | - Amol Raheja
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Ashish Suri
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
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Meade SM, Salas-Vega S, Nagy MR, Sundar SJ, Steinmetz MP, Benzel EC, Habboub G. A Pilot Remote Curriculum to Enhance Resident and Medical Student Understanding of Machine Learning in Healthcare. World Neurosurg 2023; 180:e142-e148. [PMID: 37696433 DOI: 10.1016/j.wneu.2023.09.012] [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: 05/19/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND Despite the expanding role of machine learning (ML) in health care and patient expectations for clinicians to understand ML-based tools, few for-credit curricula exist specifically for neurosurgical trainees to learn basic principles and implications of ML for medical research and clinical practice. We implemented a novel, remotely delivered curriculum designed to develop literacy in ML for neurosurgical trainees. METHODS A 4-week pilot medical elective was designed specifically for trainees to build literacy in basic ML concepts. Qualitative feedback from interested and enrolled students was collected to assess students' and trainees' reactions, learning, and future application of course content. RESULTS Despite 15 interested learners, only 3 medical students and 1 neurosurgical resident completed the course. Enrollment included students and trainees from 3 different institutions. All learners who completed the course found the lectures relevant to their future practice as clinicians and researchers and reported improved confidence in applying and understanding published literature applying ML techniques in health care. Barriers to ample enrollment and retention (e.g., balancing clinical responsibilities) were identified. CONCLUSIONS This pilot elective demonstrated the interest, value, and feasibility of a remote elective to establish ML literacy; however, feedback to increase accessibility and flexibility of the course encouraged our team to implement changes. Future elective iterations will have a semiannual, 2-week format, splitting lectures more clearly between theory (the method and its value) and application (coding instructions) and will make lectures open-source prerequisites to allow tailoring of student learning to their planned application of these methods in their practice and research.
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Affiliation(s)
- Seth M Meade
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA.
| | - Sebastian Salas-Vega
- Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA; Department of Neurosurgery, Inova Health System, Falls Church, Virginia, USA
| | - Matthew R Nagy
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Swetha J Sundar
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Michael P Steinmetz
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Edward C Benzel
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Ghaith Habboub
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, 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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Guerra GA, Hofmann H, Sobhani S, Hofmann G, Gomez D, Soroudi D, Hopkins BS, Dallas J, Pangal DJ, Cheok S, Nguyen VN, Mack WJ, Zada G. GPT-4 Artificial Intelligence Model Outperforms ChatGPT, Medical Students, and Neurosurgery Residents on Neurosurgery Written Board-Like Questions. World Neurosurg 2023; 179:e160-e165. [PMID: 37597659 DOI: 10.1016/j.wneu.2023.08.042] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Artificial intelligence (AI) and machine learning have transformed health care with applications in various specialized fields. Neurosurgery can benefit from artificial intelligence in surgical planning, predicting patient outcomes, and analyzing neuroimaging data. GPT-4, an updated language model with additional training parameters, has exhibited exceptional performance on standardized exams. This study examines GPT-4's competence on neurosurgical board-style questions, comparing its performance with medical students and residents, to explore its potential in medical education and clinical decision-making. METHODS GPT-4's performance was examined on 643 Congress of Neurological Surgeons Self-Assessment Neurosurgery Exam (SANS) board-style questions from various neurosurgery subspecialties. Of these, 477 were text-based and 166 contained images. GPT-4 refused to answer 52 questions that contained no text. The remaining 591 questions were inputted into GPT-4, and its performance was evaluated based on first-time responses. Raw scores were analyzed across subspecialties and question types, and then compared to previous findings on Chat Generative pre-trained transformer performance against SANS users, medical students, and neurosurgery residents. RESULTS GPT-4 attempted 91.9% of Congress of Neurological Surgeons SANS questions and achieved 76.6% accuracy. The model's accuracy increased to 79.0% for text-only questions. GPT-4 outperformed Chat Generative pre-trained transformer (P < 0.001) and scored highest in pain/peripheral nerve (84%) and lowest in spine (73%) categories. It exceeded the performance of medical students (26.3%), neurosurgery residents (61.5%), and the national average of SANS users (69.3%) across all categories. CONCLUSIONS GPT-4 significantly outperformed medical students, neurosurgery residents, and the national average of SANS users. The mode's accuracy suggests potential applications in educational settings and clinical decision-making, enhancing provider efficiency, and improving patient care.
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Affiliation(s)
- Gage A Guerra
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA.
| | - Hayden Hofmann
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Sina Sobhani
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Grady Hofmann
- Department of Biology, Stanford University, Palo Alto, California, USA
| | - David Gomez
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Daniel Soroudi
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Benjamin S Hopkins
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Jonathan Dallas
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Dhiraj J Pangal
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Stephanie Cheok
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Vincent N Nguyen
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - William J Mack
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Gabriel Zada
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
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Duey AH, Rana A, Siddi F, Hussein H, Onnela JP, Smith TR. Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease. Neurosurgery 2023; 93:670-677. [PMID: 36995101 DOI: 10.1227/neu.0000000000002474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/02/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Pain evaluation remains largely subjective in neurosurgical practice, but machine learning provides the potential for objective pain assessment tools. OBJECTIVE To predict daily pain levels using speech recordings from personal smartphones of a cohort of patients with diagnosed neurological spine disease. METHODS Patients with spine disease were enrolled through a general neurosurgical clinic with approval from the institutional ethics committee. At-home pain surveys and speech recordings were administered at regular intervals through the Beiwe smartphone application. Praat audio features were extracted from the speech recordings to be used as input to a K-nearest neighbors (KNN) machine learning model. The pain scores were transformed from a 0 to 10 scale to low and high pain for better discriminative capacity. RESULTS A total of 60 patients were enrolled, and 384 observations were used to train and test the prediction model. Using the KNN prediction model, an accuracy of 71% with a positive predictive value of 0.71 was achieved in classifying pain intensity into high and low. The model showed 0.71 precision for high pain and 0.70 precision for low pain. Recall of high pain was 0.74, and recall of low pain was 0.67. The overall F1 score was 0.73. CONCLUSION Our study uses a KNN to model the relationship between speech features and pain levels collected from personal smartphones of patients with spine disease. The proposed model is a stepping stone for the development of objective pain assessment in neurosurgery clinical practice.
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Affiliation(s)
- Akiro H Duey
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
- Icahn School of Medicine at Mount Sinai, New York , New York , USA
| | - Aakanksha Rana
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge , Massachusetts , USA
| | - Francesca Siddi
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
- Departments of Neurosurgery, Leiden University Medical Center, Leiden , The Netherlands
| | - Helweh Hussein
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston , Massachusetts , USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston , Massachusetts , USA
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Sevgi UT, Erol G, Doğruel Y, Sönmez OF, Tubbs RS, Güngor A. The role of an open artificial intelligence platform in modern neurosurgical education: a preliminary study. Neurosurg Rev 2023; 46:86. [PMID: 37059815 DOI: 10.1007/s10143-023-01998-2] [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: 02/09/2023] [Revised: 02/09/2023] [Accepted: 04/08/2023] [Indexed: 04/16/2023]
Abstract
The use of artificial intelligence in neurosurgical education has been growing in recent times. ChatGPT, a free and easily accessible language model, has been gaining popularity as an alternative education method. It is necessary to explore the potential of this program in neurosurgery education and to evaluate its reliability. This study aimed to show the reliability of ChatGPT by asking various questions to the chat engine, how it can contribute to neurosurgery education by preparing case reports or questions, and its contributions when writing academic articles. The results of the study showed that while ChatGPT provided intriguing and interesting responses, it should not be considered a dependable source of information. The absence of citations for scientific queries raises doubts about the credibility of the answers provided. Therefore, it is not advisable to solely rely on ChatGPT as an educational resource. With further updates and more specific prompts, it may be possible to improve its accuracy. In conclusion, while ChatGPT has potential as an educational tool, its reliability needs to be further evaluated and improved before it can be widely adopted in neurosurgical education.
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Affiliation(s)
- Umut Tan Sevgi
- Department of Neurosurgery, University of Health Sciences, Tepecik Training and Research Hospital, Izmir, Turkey
- Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy Laboratory, Istanbul, Turkey
| | - Gökberk Erol
- Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy Laboratory, Istanbul, Turkey
- Department of Neurosurgery, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Yücel Doğruel
- Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy Laboratory, Istanbul, Turkey
- The Neurosurgical Atlas, Carmel, IN, USA
| | - Osman Fikret Sönmez
- Department of Neurosurgery, University of Health Sciences, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Richard Shane Tubbs
- Department of Neurosurgery, Tulane Center for Clinical Neurosciences, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Anatomical Sciences, St. George's University, St. George's, West Indies, Grenada
- Department of Structural and Cellular Biology, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Neurosurgery and Ochsner Neuroscience Institute, Ochsner Health System, New Orleans, LA, USA
- Department of Neurology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Abuzer Güngor
- Department of Neurosurgery, Yeditepe University Microsurgical Neuroanatomy Laboratory, Istanbul, Turkey.
- Department of Neurosurgery, University of Health Sciences, Bakirkoy Research and Training Hospital for Neurology, Neurosurgery and Psychiatry, Istanbul, Turkey.
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D'Amico RS, White TG, Shah HA, Langer DJ. I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…. Neurosurgery 2023; 92:663-664. [PMID: 36757199 DOI: 10.1227/neu.0000000000002414] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 02/10/2023] Open
Affiliation(s)
- Randy S D'Amico
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
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Alizadeh B, Alibabaei A, Ahmadi S, Maroufi SF, Ghafouri-Fard S, Nateghinia S. Designing predictive models for appraisal of outcome of neurosurgery patients using machine learning-based techniques. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2022.101658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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12
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Meaney C, Das S, Colak E, Kohandel M. Deep learning characterization of brain tumours with diffusion weighted imaging. J Theor Biol 2023; 557:111342. [PMID: 36368560 DOI: 10.1016/j.jtbi.2022.111342] [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: 04/28/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour forecasting challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. Our method uses two sets of multi sequence MRI in order to produce estimations and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We first apply our deep learning model to synthetic tumours to showcase the model's capabilities and identify situations where prediction errors are likely to occur. We then apply our model to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. Our work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments.
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Affiliation(s)
- Cameron Meaney
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada.
| | - Sunit Das
- Division of Neurosurgery, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Errol Colak
- Faculty of Medicine, University of Toronto, Toronto, Canada; Department of Medical Imaging and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; Odette Professorship in Artificial Intelligence for Medical Imaging, St. Michael's Hospital, Toronto, Canada
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
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13
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Dietz N, Vaitheesh Jaganathan, Alkin V, Mettille J, Boakye M, Drazin D. Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review. J Clin Orthop Trauma 2022; 35:102046. [PMID: 36425281 PMCID: PMC9678757 DOI: 10.1016/j.jcot.2022.102046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/23/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Background Machine learning has been applied to improve diagnosis and prognostication of acute traumatic spinal cord injury. We investigate potential for clinical integration of machine learning in this patient population to navigate variability in injury and recovery. Materials and methods We performed a systematic review using PRISMA guidelines through PubMed database to identify studies that use machine learning algorithms for clinical application toward improvements in diagnosis, management, and predictive modeling. Results Of the 132 records identified, a total of 13 articles met inclusion criteria and were included in final analysis. Of the 13 articles, 5 focused on diagnostic accuracy and 8 were related to prognostication or management of traumatic spinal cord injury. Across studies, 1983 patients with spinal cord injury were evaluated with most classifying as ASIA C or D. Retrospective designs were used in 10 of 13 studies and 3 were prospective. Studies focused on MRI evaluation and segmentation for diagnostic accuracy and prognostication, investigation of mean arterial pressure in acute care and intraoperative settings, prediction of ambulatory and functional ability, chronic complication prevention, and psychological quality of life assessments. Decision tree, random forests (RF), support vector machines (SVM), hierarchical cluster tree analysis (HCTA), artificial neural networks (ANN), convolutional neural networks (CNN) machine learning subtypes were used. Conclusions Machine learning represents a platform technology with clinical application in traumatic spinal cord injury diagnosis, prognostication, management, rehabilitation, and risk prevention of chronic complications and mental illness. SVM models showed improved accuracy when compared to other ML subtypes surveyed. Inherent variability across patients with SCI offers unique opportunity for ML and personalized medicine to drive desired outcomes and assess risks in this patient population.
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Affiliation(s)
- Nicholas Dietz
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Vaitheesh Jaganathan
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | | | - Jersey Mettille
- Department of Anesthesia, University of Louisville, Louisville, KY, USA
| | - Maxwell Boakye
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Doniel Drazin
- Department of Neurosurgery, Providence Regional Medical Center Everett, Everett, WA, USA
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14
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Velagapudi L, Saiegh FA, Swaminathan S, Mouchtouris N, Khanna O, Sabourin V, Gooch MR, Herial N, Tjoumakaris S, Rosenwasser RH, Jabbour P. Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions. Clin Neurol Neurosurg 2022; 224:107547. [PMID: 36481326 DOI: 10.1016/j.clineuro.2022.107547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/02/2022] [Accepted: 11/24/2022] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Machine learning algorithms have received increased attention in neurosurgical literature for improved accuracy over traditional predictive methods. In this review, the authors sought to assess current applications of machine learning for outcome prediction of neurosurgical treatment of intracranial aneurysms and identify areas for future research. METHODS A PRISMA-compliant systematic review of the PubMed, MEDLINE, and EMBASE databases was conducted for all studies utilizing machine learning for outcome prediction of intracranial aneurysm treatment. Patient characteristics, machine learning methods, outcomes of interest, and accuracy metrics were recorded from included studies. RESULTS 16 studies were ultimately included in qualitative synthesis. Studies primarily analyzed angiographic outcomes, functional outcomes, or complication prediction using clinical, radiological, or composite variables. The majority of included studies utilized supervised learning algorithms for analysis of dichotomized outcomes. CONCLUSIONS Commonly included variables were demographics, presentation variables (including ruptured or unruptured status), and treatment used. Areas for future research include increased generalizability across institutions and for smaller datasets, as well as development of front-end tools for clinical applicability of published algorithms.
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Affiliation(s)
- Lohit Velagapudi
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Fadi Al Saiegh
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Shreya Swaminathan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | | | - Omaditya Khanna
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Victor Sabourin
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | - M Reid Gooch
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Nabeel Herial
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
| | | | | | - Pascal Jabbour
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA.
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15
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Ghannam MM, Davies JM. Application of Big Data in Vascular Neurosurgery. Neurosurg Clin N Am 2022; 33:469-482. [DOI: 10.1016/j.nec.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Dundar TT, Yurtsever I, Pehlivanoglu MK, Yildiz U, Eker A, Demir MA, Mutluer AS, Tektaş R, Kazan MS, Kitis S, Gokoglu A, Dogan I, Duru N. Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium. Front Surg 2022; 9:863633. [PMID: 35574559 PMCID: PMC9099011 DOI: 10.3389/fsurg.2022.863633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/28/2022] [Indexed: 01/22/2023] Open
Abstract
ObjectivesArtificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence.MethodsPreoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected.ResultsThe most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points.ConclusionsAI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.
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Affiliation(s)
- Tolga Turan Dundar
- Bezmiâlem Vakif Üniversitesi, Istanbul, Turkey
- *Correspondence: Tolga Turan Dundar
| | | | | | | | | | | | | | | | | | | | | | | | - Nevcihan Duru
- Kocaeli Health and Technology University, Başiskele, Turkey
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Unadkat V, Pangal DJ, Kugener G, Roshannai A, Chan J, Zhu Y, Markarian N, Zada G, Donoho DA. Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study. Neurosurg Focus 2022; 52:E11. [PMID: 35364576 DOI: 10.3171/2022.1.focus21652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/25/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE While the utilization of machine learning (ML) for data analysis typically requires significant technical expertise, novel platforms can deploy ML methods without requiring the user to have any coding experience (termed AutoML). The potential for these methods to be applied to neurosurgical video and surgical data science is unknown. METHODS AutoML, a code-free ML (CFML) system, was used to identify surgical instruments contained within each frame of endoscopic, endonasal intraoperative video obtained from a previously validated internal carotid injury training exercise performed on a high-fidelity cadaver model. Instrument-detection performances using CFML were compared with two state-of-the-art ML models built using the Python coding language on the same intraoperative video data set. RESULTS The CFML system successfully ingested surgical video without the use of any code. A total of 31,443 images were used to develop this model; 27,223 images were uploaded for training, 2292 images for validation, and 1928 images for testing. The mean average precision on the test set across all instruments was 0.708. The CFML model outperformed two standard object detection networks, RetinaNet and YOLOv3, which had mean average precisions of 0.669 and 0.527, respectively, in analyzing the same data set. Significant advantages to the CFML system included ease of use, relatively low cost, displays of true/false positives and negatives in a user-friendly interface, and the ability to deploy models for further analysis with ease. Significant drawbacks of the CFML model included an inability to view the structure of the trained model, an inability to update the ML model once trained with new examples, and the inability for robust downstream analysis of model performance and error modes. CONCLUSIONS This first report describes the baseline performance of CFML in an object detection task using a publicly available surgical video data set as a test bed. Compared with standard, code-based object detection networks, CFML exceeded performance standards. This finding is encouraging for surgeon-scientists seeking to perform object detection tasks to answer clinical questions, perform quality improvement, and develop novel research ideas. The limited interpretability and customization of CFML models remain ongoing challenges. With the further development of code-free platforms, CFML will become increasingly important across biomedical research. Using CFML, surgeons without significant coding experience can perform exploratory ML analyses rapidly and efficiently.
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Affiliation(s)
- Vyom Unadkat
- 1Department of Computer Science, USC Viterbi School of Engineering, Los Angeles, California.,2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Dhiraj J Pangal
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Guillaume Kugener
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Arman Roshannai
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Justin Chan
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Yichao Zhu
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Nicholas Markarian
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Gabriel Zada
- 2Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, California; and
| | - Daniel A Donoho
- 3Division of Neurosurgery, Center for Neurosciences, Children's National Hospital, Washington, DC
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Tang OY, Pugacheva A, Bajaj AI, Rivera Perla KM, Weil RJ, Toms SA. The National Inpatient Sample: A Primer for Neurosurgical Big Data Research and Systematic Review. World Neurosurg 2022; 162:e198-e217. [PMID: 35247618 DOI: 10.1016/j.wneu.2022.02.113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/25/2022] [Accepted: 02/26/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The National Inpatient Sample - the largest all-payer inpatient database in the United States - is an important instrument for big data analysis of neurosurgical inquiries. However, earlier research has determined that many NIS studies are limited by common methodological pitfalls. In this study, we provide the first primer of NIS methodological procedures in the setting of neurosurgical research and review all published neurosurgical studies utilizing the NIS. METHODS We designed a protocol for neurosurgical big data research using the NIS, based on the authors' subject matter expertise, NIS documentation, and input and verification from the Healthcare Cost and Utilization Project. We subsequently used a comprehensive search strategy to identify all neurosurgical studies utilizing the NIS in the PubMed and MEDLINE, Embase, and Web of Science databases from inception to August 2021. Studies underwent qualitative categorization (years of the NIS studied, neurosurgical subspecialty, age group, and thematic focus of study objective) and analysis of longitudinal trends. RESULTS We identified a canonical, four-step protocol for NIS analysis: study population selection, defining additional clinical variables, identification and coding of outcomes, and statistical analysis. Methodological nuances discussed include identifying neurosurgery-specific admissions, addressing missing data, calculating additional severity and hospital-specific metrics, coding perioperative complications, and applying survey weights to make nationwide estimates. Inherent database limitations and common pitfalls of NIS studies discussed include lack of disease process-specific variables and data following the index admission, inability to calculate certain hospital-specific variables after 2011, performing state-level analyses, conflating hospitalization charges and costs, and not following proper statistical methodology for performing survey-weighted regression. In a systematic review, we identified 647 neurosurgical studies utilizing the NIS. While almost 60% of studies were published after 2015, <10% of studies analyzed NIS data after 2015. The average sample size of studies was 507,352 patients (standard deviation=2,739,900). Most studies analyzed cranial procedures (58.1%) and adults (68.1%). The most prevalent topic areas analyzed were surgical outcome trends (35.7%) and health policy and economics (17.8%), while patient disparities (9.4%) and surgeon or hospital volume (6.6%) were the least studied. CONCLUSIONS We present a standardized methodology to analyze the NIS, systematically review the state of the NIS neurosurgical literature, suggest potential future directions for neurosurgical big data inquiries, and outline recommendations to improve the design of future neurosurgical data instruments.
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Affiliation(s)
- Oliver Y Tang
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Alisa Pugacheva
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Ankush I Bajaj
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Krissia M Rivera Perla
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Harvard T.H Chan School of Public Health, Boston, MA, USA
| | - Robert J Weil
- Southcoast Brain & Spine, Southcoast Health, Dartmouth, MA, USA
| | - Steven A Toms
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA.
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19
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Bray DP, Saad H, Douglas JM, Grogan D, Dawoud RA, Chow J, Deibert C, Pradilla G, Nduom EK, Olson JJ, Alawieh AM, Hoang KB. Artificial neural networks predict the need for permanent cerebrospinal fluid diversion following posterior fossa tumor resection. Neurooncol Adv 2022; 4:vdac145. [PMID: 36299798 PMCID: PMC9586212 DOI: 10.1093/noajnl/vdac145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Resection of posterior fossa tumors (PFTs) can result in hydrocephalus that requires permanent cerebrospinal fluid (CSF) diversion. Our goal was to prospectively validate a machine-learning model to predict postoperative hydrocephalus after PFT surgery requiring permanent CSF diversion. Methods We collected preoperative and postoperative variables on 518 patients that underwent PFT surgery at our center in a retrospective fashion to train several statistical classifiers to predict the need for permanent CSF diversion as a binary class. A total of 62 classifiers relevant to our data structure were surveyed, including regression models, decision trees, Bayesian models, and multilayer perceptron artificial neural networks (ANN). Models were trained using the (N = 518) retrospective data using 10-fold cross-validation to obtain accuracy metrics. Given the low incidence of our positive outcome (12%), we used the positive predictive value along with the area under the receiver operating characteristic curve (AUC) to compare models. The best performing model was then prospectively validated on a set of 90 patients. Results Twelve percent of patients required permanent CSF diversion after PFT surgery. Of the trained models, 8 classifiers had an AUC greater than 0.5 on prospective testing. ANNs demonstrated the highest AUC of 0.902 with a positive predictive value of 83.3%. Despite comparable AUC, the remaining classifiers had a true positive rate below 35% (compared to ANN, P < .0001). The negative predictive value of the ANN model was 98.8%. Conclusions ANN-based models can reliably predict the need for ventriculoperitoneal shunt after PFT surgery.
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Affiliation(s)
- David P Bray
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Hassan Saad
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Dayton Grogan
- Medical College of Georgia-Augusta University, Augusta, Georgia, USA
| | | | - Jocelyn Chow
- College of Arts and Sciences, Emory University, Atlanta, Georgia, USA
| | - Christopher Deibert
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Gustavo Pradilla
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Edjah K Nduom
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jeffrey J Olson
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Ali M Alawieh
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kimberly B Hoang
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
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Lim MJR. Letter: Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:E333-E334. [PMID: 34498686 DOI: 10.1093/neuros/nyab337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Mervyn J R Lim
- Division of Neurosurgery University Surgical Centre National University Hospital Singapore
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21
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Dagi TF, Barker Ii FG, Glass J. In Reply: Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:E335. [PMID: 34510215 DOI: 10.1093/neuros/nyab349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- T Forcht Dagi
- Queen's University Belfast and The William J. Clinton Leadership Institute Belfast, UK.,Mayo Medical School Rochester, Minnesota, USA
| | - Fred G Barker Ii
- Department of Neurosurgery Harvard Medical School The Massachusetts General Hospital Boston Massachusetts, USA
| | - Jacob Glass
- Center for Epigenetics Research Memorial Sloan Kettering Cancer Center New York, New York, USA
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