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Lampros M, Symeou S, Vlachos N, Gkampenis A, Zigouris A, Voulgaris S, Alexiou GA. Applications of machine learning in pediatric traumatic brain injury (pTBI): a systematic review of the literature. Neurosurg Rev 2024; 47:737. [PMID: 39367894 DOI: 10.1007/s10143-024-02955-3] [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: 05/27/2024] [Revised: 09/21/2024] [Accepted: 09/28/2024] [Indexed: 10/07/2024]
Abstract
OBJECTIVE Pediatric traumatic brain injury (pTBI) is a heterogeneous condition requiring the development of clinical decision rules (CDRs) for the optimal management of these patients. Machine learning (ML) is a novel artificial intelligence (AI) predictive tool with various applications in modern neurosurgery, including the creation of CDRs for patients with pTBI. In the present study, we summarized the current literature on the applications of ML in pTBI. METHODS A systematic review was conducted following the PRISMA guidelines. The literature search included PubMed/MEDLINE, SCOPUS, and ScienceDirect databases. We included observational or experimental studies focusing on the applications of ML in patients with pTBI under 18 years of age. RESULTS A total of 18 articles were included in our systematic review. Of these articles, 16 were retrospective cohorts, 1 was a prospective cohort, and 1 was a case-control study. Of these articles, ten concerned ML applications in predicting the outcome of pTBI patients, while 8 reported applications of ML in predicting the need for CT scans. Artificial Neuronal Network (ANN) and Random Forest (RF) were the most commonly utilized models for the creation of predictive algorithms. The accuracy of the ML algorithms to predict the need for CT scan in pTBI cases ranged from 0.790 to 0.999, and the Area Under Curve (AUC) ranged from 0.411 (95%CI: 0.354-0.468) to 0.980 (95%CI: 0.950-1.00). The model with the maximum accuracy to predict the need for CT scan was a Deep ANN model, while the model with the maximum AUC was Ensemble Learning. The model with the maximum accuracy to predict the outcome (favorable vs. unfavorable) of patients with TBI was a support vector machine (SVM) model with 94.0% accuracy, whereas the model with the highest AUC was an ANN model with an AUC of 0.991. CONCLUSION In the present systematic review, conventional and novel ML models were utilized to either predict the presence of intracranial trauma or the prognosis of children with pTBI. However, most of the reported ML algorithms have not been externally validated and are pending further research.
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Affiliation(s)
- Marios Lampros
- Department of Neurosurgery, University Hospital of Ioannina, Ioannina, Greece
- Medical School, University of Ioannina, Ioannina, Greece
| | - Solonas Symeou
- Medical School, University of Ioannina, Ioannina, Greece
| | - Nikolaos Vlachos
- Department of General Surgery, Hatzikosta General Hospital, Ioannina, Greece
| | | | - Andreas Zigouris
- Department of Neurosurgery, University Hospital of Ioannina, Ioannina, Greece
| | - Spyridon Voulgaris
- Department of Neurosurgery, University Hospital of Ioannina, Ioannina, Greece
- Medical School, University of Ioannina, Ioannina, Greece
| | - George A Alexiou
- Department of Neurosurgery, University Hospital of Ioannina, Ioannina, Greece.
- Medical School, University of Ioannina, Ioannina, Greece.
- Department of Neurosurgery, University of Ioannina School of Medicine, S. Niarhou Avenue, Ioannina, 45500, Greece.
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Abdelwahab SI, Taha MME, Alfaifi HA, Farasani A, Hassan W. Bibliometric Analysis of Machine Learning Applications in Ischemia Research. Curr Probl Cardiol 2024; 49:102754. [PMID: 39079619 DOI: 10.1016/j.cpcardiol.2024.102754] [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: 07/13/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024]
Abstract
OBJECTIVE The objective of this study is to conduct a comprehensive bibliometric analysis to elucidate the landscape of machine learning applications in ischemia research. METHODS The analysis can be divided in three sections: part 1 scrutinizes articles and reviews with "ischemia" in their titles, while part 2 further narrows the focus to publications containing both "ischemia" and "machine learning" in their titles. Additionally, part 3 delves into the examination of the top 50 most cited papers, exploring their thematic focus and co-word dynamics. RESULTS The findings reveal a significant increase in publications over the years, with notable trends identified through detailed analysis. The growth in publication counts over time, the leading contributors, institutions, geographical distribution of research output and journals are numerically presented for part 1 and part 2. For the top 50 most cited papers the dynamics of co-words, which offer a nuanced understanding of thematic trends and emerging concepts, are presented. Based on the number of citations the top 10 authors were selected, and later for each, total number of publications, h-index, g-index and m-index are provided. Additionally, figures depicting the co-authorship network among authors, departments, and countries involved in the top 50 cited papers may enrich our comprehension of collaborative networks in ischemia research. CONCLUSION This comprehensive bibliometric analysis provides valuable insights into the evolving landscape of machine learning applications in ischemia research.
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Affiliation(s)
| | | | - Hassan Ahmad Alfaifi
- Pharmaceutical Care Administration (Jeddah Second Health Cluster), Ministry of Health, Saudi Arabia
| | - Abdullah Farasani
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Jazan University
| | - Waseem Hassan
- Institute of Chemical Sciences, University of Peshawar, Peshawar 25120, Khyber Pakhtunkhwa, Pakistan.
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Abdollahifard S, Farrokhi A, Mowla A, Liebeskind DS. Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements. Neurol Clin 2024; 42:633-650. [PMID: 38937033 DOI: 10.1016/j.ncl.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medicine is not an exception to the growing usage of AI in various scientific fields. Vascular and interventional neurology deal with diseases that require early diagnosis and appropriate intervention, which are crucial to saving patients' lives. In these settings, AI can be an extra pair of hands for physicians or in conditions where there is a shortage of clinical experts. In this article, the authors have reviewed the common metrics used in interpreting the performance of models and common algorithms used in this field.
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Affiliation(s)
- Saeed Abdollahifard
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Research Center for Neuromodulation and Pain, Shiraz, Iran
| | | | - Ashkan Mowla
- Division of Stroke and Endovascular Neurosurgery, Department of Neurological Surgery, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | - David S Liebeskind
- UCLA Department of Neurology, Neurovascular Imaging Research Core, UCLA Comprehensive Stroke Center, University of California Los Angeles(UCLA), CA, USA.
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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; 187:e769-e791. [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] [MESH Headings] [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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Shlobin NA, Rosseau G. Opportunities and Considerations for the Incorporation of Artificial Intelligence into Global Neurosurgery: A Generative Pretrained Transformer Chatbot-Based Approach. World Neurosurg 2024; 186:e398-e412. [PMID: 38561032 DOI: 10.1016/j.wneu.2024.03.149] [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: 02/29/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Global neurosurgery is a public health focus in neurosurgery that seeks to ensure safe, timely, and affordable neurosurgical care to all individuals worldwide. Although investigators have begun to explore the promise of artificial intelligence (AI) for neurosurgery, its applicability to global neurosurgery has been largely hypothetical. We characterize opportunities and considerations for the incorporation of AI into global neurosurgery by synthesizing key themes yielded from a series of generative pretrained transformers (GPTs), discuss important limitations of GPTs and cautions when using AI in neurosurgery, and develop a framework for the equitable incorporation of AI into global neurosurgery. METHODS ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can AI be incorporated into global neurosurgery?" A layered ChatGPT-based thematic analysis was performed. The authors synthesized the results into opportunities and considerations for the incorporation of AI in global neurosurgery. A Pareto analysis was conducted to determine common themes. RESULTS Eight opportunities and 14 important considerations were synthesized. Six opportunities related to patient care, 1 to education, and another to public health planning. Four of the important considerations were deemed specific to global neurosurgery. The Pareto analysis included all 8 opportunities and 5 considerations. CONCLUSIONS AI may be incorporated into global neurosurgery in a variety of capacities requiring numerous considerations. The framework presented in this manuscript may facilitate the incorporation of AI into global neurosurgery initiatives while balancing contextual factors and the reality of limited resources.
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Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
| | - Gail Rosseau
- Department of Neurosurgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA; Barrow Global, Barrow Neurological Institute, Phoenix, Arizona, USA
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Sitaraman S, Pahwa B, Chaurasia B. Letter to the Editor Regarding "Exploring the Landscape of Machine Learning Applications in Neurosurgery: A Bibliometric Analysis and Narrative Review of Trends and Future Directions". World Neurosurg 2024; 186:266. [PMID: 38849995 DOI: 10.1016/j.wneu.2024.02.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Sudharman Sitaraman
- University College of Medical Sciences and G.T.B. Hospital, New Delhi, India
| | - Bhavya Pahwa
- University College of Medical Sciences and G.T.B. Hospital, New Delhi, India.
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Ge S, Chen J, Wang W, Zhang LB, Teng Y, Yang C, Wang H, Tao Y, Chen Z, Li R, Niu Y, Zuo C, Tan L. Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study. BMC Neurol 2024; 24:177. [PMID: 38802769 PMCID: PMC11129362 DOI: 10.1186/s12883-024-03630-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/08/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.
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Affiliation(s)
- Sihan Ge
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Junxin Chen
- School of Software, Dalian University of Technology, Dalian, China
| | - Wei Wang
- Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Li-Bo Zhang
- Department of Radiology, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang, China
| | - Yue Teng
- Emergency Department, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang, China
| | - Cheng Yang
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Hao Wang
- Department of Neurosurgery, Daping Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Yihao Tao
- Department of Neurosurgery, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Zhi Chen
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Ronghao Li
- Department of Basic Medicine, Army Medical University, Chongqing, China
| | - Yin Niu
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
| | - Chenghai Zuo
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
| | - Liang Tan
- Department of Critical Care Medicine, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
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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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Alrawashdeh A, Alqahtani S, Alkhatib ZI, Kheirallah K, Melhem NY, Alwidyan M, Al-Dekah AM, Alshammari T, Nehme Z. Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review. Prehosp Disaster Med 2024:1-11. [PMID: 38757150 DOI: 10.1017/s1049023x24000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
OBJECTIVE The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). METHODS Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. RESULTS This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. CONCLUSION Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.
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Affiliation(s)
- Ahmad Alrawashdeh
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Saeed Alqahtani
- Department of Emergency Medical Services, Prince Sultan Military College for Health Sciences, Dhahran, Saudi Arabia
| | - Zaid I Alkhatib
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Khalid Kheirallah
- Department of Public Health and Family Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nebras Y Melhem
- Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Mahmoud Alwidyan
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | | | - Talal Alshammari
- Department of Emergency Medical Care, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ziad Nehme
- Ambulance Victoria, Doncaster, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Kapila AK, Hamdi M. Time to Evolve Plastic Surgery Education? Integrating Robotic Techniques and Artificial Intelligence into Training Curricula. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2024; 12:e5778. [PMID: 38699284 PMCID: PMC11062664 DOI: 10.1097/gox.0000000000005778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Affiliation(s)
- Ayush K. Kapila
- From the Department of Plastic, Reconstructive and Aesthetic Surgery, Brussels University Hospital (UZ Brussel), Brussels, Belgium
| | - Moustapha Hamdi
- From the Department of Plastic, Reconstructive and Aesthetic Surgery, Brussels University Hospital (UZ Brussel), Brussels, Belgium
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Upreti G. Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence. Indian J Otolaryngol Head Neck Surg 2024; 76:2184-2190. [PMID: 38566692 PMCID: PMC10982213 DOI: 10.1007/s12070-023-04415-8] [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: 10/16/2023] [Accepted: 11/28/2023] [Indexed: 04/04/2024] Open
Abstract
Purpose This narrative review examines the evolving landscape of artificial intelligence (AI) integration in skull base surgery, exploring its multifaceted applications and impact on various aspects of patient care. Methods Extensive literature review was conducted to gather insights into the role of AI in skull base surgery. Key aspects such as diagnosis, image analysis, surgical planning, navigation, predictive analytics, clinical decision-making, postoperative care, rehabilitation, and virtual simulations were explored. Studies were sourced from PubMed using keyword search strategy for relevant headings, sub-headings and cross-referencing. Results AI enhances early diagnosis through diagnostic algorithms that guide investigations based on clinical and radiological data. AI-driven image analysis enables accurate segmentation of intricate structures and extraction of radiomics data, optimizing preoperative planning and predicting treatment response. In surgical planning, AI aids in identifying critical structures, leading to precise interventions. Real-time AI-based navigation offers adaptive guidance, enhancing surgical accuracy and safety. Predictive analytics empower risk assessment, treatment planning, and outcome prediction. AI-driven clinical decision support systems optimize resource allocation and support shared decision-making. Postoperative care benefits from AI's monitoring capabilities and personalized rehabilitation protocols. Virtual simulations powered by AI expedite skill development and decision-making in complex procedures. Conclusion AI contributes to accurate diagnosis, surgical planning, navigation, predictive analysis, and postoperative care. Ethical considerations and data quality assurance are essential, ensuring responsible AI implementation. While AI serves as a valuable complement to clinical expertise, its potential to enhance decision-making, precision, and efficiency in skull base surgery is evident.
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Affiliation(s)
- Garima Upreti
- Department of Otorhinolaryngology, All India Institute of Medical Sciences, Rajkot, Gujarat India
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12
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Hui P, Jiang Y, Wang J, Wang C, Li Y, Fang B, Wang H, Wang Y, Qie S. Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease. Front Aging Neurosci 2024; 16:1354147. [PMID: 38524116 PMCID: PMC10957543 DOI: 10.3389/fnagi.2024.1354147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/22/2024] [Indexed: 03/26/2024] Open
Abstract
Background Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention. Objective The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice. Methods Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability. Results The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08. Conclusion The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.
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Affiliation(s)
- Pengsheng Hui
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yu Jiang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Congxiao Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yingqi Li
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Boyan Fang
- Department of Neurological Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Hujun Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yingpeng Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Shuyan Qie
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
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Qian Y, Alhaskawi A, Dong Y, Ni J, Abdalbary S, Lu H. Transforming medicine: artificial intelligence integration in the peripheral nervous system. Front Neurol 2024; 15:1332048. [PMID: 38419700 PMCID: PMC10899496 DOI: 10.3389/fneur.2024.1332048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.
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Affiliation(s)
- Yue Qian
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Ahmad Alhaskawi
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yanzhao Dong
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Juemin Ni
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Sahar Abdalbary
- Department of Orthopedic Physical Therapy, Faculty of Physical Therapy, Nahda University in Beni Suef, Beni Suef, Egypt
| | - Hui Lu
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, China
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14
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Buchlak QD, Tang CHM, Seah JCY, Johnson A, Holt X, Bottrell GM, Wardman JB, Samarasinghe G, Dos Santos Pinheiro L, Xia H, Ahmad HK, Pham H, Chiang JI, Ektas N, Milne MR, Chiu CHY, Hachey B, Ryan MK, Johnston BP, Esmaili N, Bennett C, Goldschlager T, Hall J, Vo DT, Oakden-Rayner L, Leveque JC, Farrokhi F, Abramson RG, Jones CM, Edelstein S, Brotchie P. Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy. Eur Radiol 2024; 34:810-822. [PMID: 37606663 PMCID: PMC10853361 DOI: 10.1007/s00330-023-10074-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/16/2023] [Accepted: 07/01/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVES Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists. METHODS A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard. RESULTS The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced. CONCLUSIONS The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation. CLINICAL RELEVANCE STATEMENT This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care. KEY POINTS • This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.
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Affiliation(s)
- Quinlan D Buchlak
- Annalise.ai, Sydney, NSW, Australia.
- School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia.
- Department of Neurosurgery, Monash Health, Clayton, VIC, Australia.
| | | | - Jarrel C Y Seah
- Annalise.ai, Sydney, NSW, Australia
- Department of Radiology, Alfred Health, Melbourne, VIC, Australia
| | | | | | | | | | | | | | | | | | - Hung Pham
- Annalise.ai, Sydney, NSW, Australia
- Department of Radiology, University Medical Center, University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam
| | - Jason I Chiang
- Annalise.ai, Sydney, NSW, Australia
- Department of General Practice, University of Melbourne, Melbourne, VIC, Australia
- Westmead Applied Research Centre, University of Sydney, Sydney, NSW, Australia
| | | | | | | | | | | | | | - Nazanin Esmaili
- School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - Christine Bennett
- School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia
| | - Tony Goldschlager
- Department of Neurosurgery, Monash Health, Clayton, VIC, Australia
- Department of Surgery, Monash University, Clayton, VIC, Australia
| | - Jonathan Hall
- Annalise.ai, Sydney, NSW, Australia
- Department of Radiology, St Vincent's Health Australia, Melbourne, VIC, Australia
- Department of Radiology, Austin Hospital, Melbourne, VIC, Australia
| | - Duc Tan Vo
- Department of Radiology, University Medical Center, University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | | | - Farrokh Farrokhi
- Center for Neurosciences and Spine, Virginia Mason Franciscan Health, Seattle, WA, USA
| | | | - Catherine M Jones
- Annalise.ai, Sydney, NSW, Australia
- I-MED Radiology Network, Brisbane, QLD, Australia
- School of Public and Preventive Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Imaging Science, University of Sydney, Sydney, NSW, Australia
| | - Simon Edelstein
- Annalise.ai, Sydney, NSW, Australia
- I-MED Radiology Network, Brisbane, QLD, Australia
- Department of Radiology, Monash Health, Clayton, VIC, Australia
| | - Peter Brotchie
- Annalise.ai, Sydney, NSW, Australia
- Department of Radiology, St Vincent's Health Australia, Melbourne, VIC, Australia
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15
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Zeitler DM, Buchlak QD, Ramasundara S, Farrokhi F, Esmaili N. Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning. Laryngoscope 2024; 134:926-936. [PMID: 37449725 DOI: 10.1002/lary.30894] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/24/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVES The aim of the study was to train and test supervised machine-learning classifiers to predict acoustic hearing preservation after CI using preoperative clinical data. STUDY DESIGN Retrospective predictive modeling study of prospectively collected single-institution CI dataset. METHODS One hundred and seventy-five patients from a REDCap database including 761 patients >18 years who underwent CI and had audiometric testing preoperatively and one month after surgery were included. The primary outcome variable was the lowest quartile change in acoustic hearing at one month after CI using various formulae (standard pure tone average, SPTA; low-frequency PTA, LFPTA). Analysis involved applying multivariate logistic regression to detect statistical associations and training and testing supervised learning classifiers. Classifier performance was assessed with numerous metrics including area under the receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC). RESULTS Lowest quartile change (indicating hearing preservation) in SPTA was positively associated with a history of meningitis, preoperative LFPTA, and preoperative SPTA. Lowest quartile change in SPTA was negatively associated with sudden hearing loss, noise exposure, aural fullness, and abnormal anatomy. Lowest quartile change in LFPTA was positively associated with preoperative LFPTA. Lowest quartile change in LFPTA was negatively associated with tobacco use. Random forest demonstrated the highest mean classification performance on the validation dataset when predicting each of the outcome variables. CONCLUSIONS Machine learning demonstrated utility for predicting preservation of residual acoustic hearing in patients undergoing CI surgery, and the detected associations facilitated the interpretation of our machine-learning models. The models and statistical associations together may be used to facilitate improvements in shared clinical decision-making and patient outcomes. LEVEL OF EVIDENCE 3 Laryngoscope, 134:926-936, 2024.
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Affiliation(s)
- Daniel M Zeitler
- Neuroscience Institute, Virginia Mason Franciscan Health, Seattle, Washington, USA
- Department of Otolaryngology-Head Neck Surgery, Section of Otology/Neurotology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Quinlan D Buchlak
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
- Department of Neurosurgery, Monash Health, Melbourne, Victoria, Australia
| | - Savindi Ramasundara
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Franciscan Health, Seattle, Washington, USA
- Department of Neurosurgery, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Nazanin Esmaili
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
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Buser Z, Meisel HJ, Agarwal N, Wu Y, Jain A, van Hooff M, Alini M, Yoon ST, Wang JC, Santesso N. Development of an International AO Spine Guideline for the Use of Osteobiologics in Anterior Cervical Fusion and Decompression (AO-GO). Global Spine J 2024; 14:14S-23S. [PMID: 38421327 PMCID: PMC10913912 DOI: 10.1177/21925682231201601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
STUDY DESIGN Methodological study for guideline development. OBJECTIVE AO Spine Guideline for Using Osteobiologics (AO-GO) project for spine degenerative pathologies was an international, multidisciplinary collaborative initiative to identify and evaluate evidence on existing use of osteobiologics in Anterior Cervical Fusion and Decompression (ACDF). The aim was to formulate precisely defined, clinically relevant and internationally applicable guidelines ensuring evidence-based, safe and effective use of osteobiologics, considering regional preferences and cost-effectiveness. METHODS Guideline was completed in two phases: Phase 1- evidence synthesis; Phase 2- recommendation development based on the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In Phase 1, key questions identified by a panel of experts were addressed in a series of systematic reviews of randomized and non-randomized studies. In Phase 2, the GRADE approach was used to formulate a series of recommendations, including expert panel discussions via web calls and face-to-face meetings. DISCUSSION AO-GO aims to bridge an important gap between evidence and use of osteobiologics in spine fusion surgeries. Owing to differences in osteobiologics preparation and functional characteristics, regulatory requirements for approval may vary, therefore it is highly likely that these products enter market without quality clinical trials. With a holistic approach the guideline aims to facilitate evidence-based, patient-oriented decision-making processes in clinical practice, thus stimulating further evidence-based studies regarding osteobiologics usage in spine surgeries. In Phase 3, the guideline will be disseminated and validated using prospectively collected clinical data in a separate effort of the AO Spine Knowledge Forum Degenerative in a global multicenter clinical study.
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Affiliation(s)
- Zorica Buser
- Department of Orthopedic Surgery, Grossman School of Medicine, NYU, NY, USA
| | | | - Neha Agarwal
- Neurosurgery, BG Klinikum Bergmannstrost Halle, Halle, Germany
| | - Yabin Wu
- AO Foundation, Davos, Switzerland
| | - Amit Jain
- Johns Hopkins Medicine, Baltimore, MD, USA
| | - Miranda van Hooff
- Department of Orthopaedic Surgery, Radboudumc, Nijmegen, Netherlands
| | - Mauro Alini
- AO Research Institute, AO Foundation, Davos, Switzerland
| | - Sangwook Tim Yoon
- Orthopedic Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Jeffrey C Wang
- Department of Orthopedic Surgery, Keck School of Medicine, USC, Los Angeles, CA, USA
| | - Nancy Santesso
- McMaster University Department of Health Research Methods Evidence and Impact, Hamilton, ON, Canada
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17
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Zhang H, Zhao J, Farzan R, Alizadeh Otaghvar H. Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review. Int Wound J 2024; 21:e14665. [PMID: 38272811 PMCID: PMC10805538 DOI: 10.1111/iwj.14665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like 'machine learning', 'surgical' and 'wound'. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn-grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.
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Affiliation(s)
- Hui Zhang
- The Second Clinical Medical SchoolLanzhou UniversityLanzhouChina
| | - Junde Zhao
- Department of Clinical Medicine, Health Science CenterLanzhou UniversityLanzhouChina
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Hamidreza Alizadeh Otaghvar
- Associate Professor of Plastic Surgery, Trauma and Injury Research CenterIran University of Medical SciencesTehranIran
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Levy AS, Bhatia S, Merenzon MA, Andryski AL, Rivera CA, Daggubati LC, Di L, Shah AH, Komotar RJ, Ivan ME. Exploring the Landscape of Machine Learning Applications in Neurosurgery: A Bibliometric Analysis and Narrative Review of Trends and Future Directions. World Neurosurg 2024; 181:108-115. [PMID: 37839564 DOI: 10.1016/j.wneu.2023.10.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/08/2023] [Accepted: 10/08/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND The field of neurosurgery has consistently represented an area of innovation and integration of technology since its inception. As such, machine learning (ML) has found its way into applications within neurosurgery relatively rapidly. Through this bibliometric review and cluster analysis, we seek to identify trends and emerging applications of ML within neurosurgery. METHODS A bibliometric analysis was carried out in the Web of Science database on publications from January 2000 to March 2023. The full data set of the 200 most cited publications including title, author information, journal, citation count, keywords, and abstracts for each publication was evaluated in CiteSpace. CiteSpace was used to elucidate publication characteristics, trends, and topic clusters via collaborate network analysis using the Kamada-Kawai algorithm. RESULTS The 25 most cited titles were included in our analysis. Harvard University and its affiliates represented the top institution, contributing nearly 25% of publications in the literature. WORLD NEUROSURGERY was the journal with the highest net citation count of 747 (29%). Collaborative network analysis generated 12 unique clusters, the largest of which was machine learning, followed by feature importance and deep brain stimulation. CONCLUSION This review highlights the most impactful articles pertaining to ML in the field of neurosurgery. ML has been applied into several sub-specialties within neurosurgery to optimize patient care, with special attention to outcome predictors, patient selection, and surgical decision making.
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Affiliation(s)
- Adam S Levy
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA.
| | - Shovan Bhatia
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Martin A Merenzon
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Allie L Andryski
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Cameron A Rivera
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Lekhaj C Daggubati
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Long Di
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Ashish H Shah
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Ricardo J Komotar
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA; Sylvester Cancer Center, University of Miami Health System, Miami, Florida, USA
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, USA; Sylvester Cancer Center, University of Miami Health System, Miami, Florida, USA
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Carrasco-Ribelles LA, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violán C, Zabaleta-del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J Am Med Inform Assoc 2023; 30:2072-2082. [PMID: 37659105 PMCID: PMC10654870 DOI: 10.1093/jamia/ocad168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/02/2023] [Accepted: 08/11/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. METHODS This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. RESULTS Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model's performance. Reporting quality was poor, and a third of the studies were at high risk of bias. CONCLUSIONS AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication. REGISTRATION PROSPERO database (CRD42022331388).
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Affiliation(s)
- Lucía A Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - José Llanes-Jurado
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politècnica de València (UPV), València, 46022, Spain
| | - Carlos Gallego-Moll
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - Margarita Cabrera-Bean
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
| | - Mònica Monteagudo-Zaragoza
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
| | - Concepción Violán
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
- Direcció d’Atenció Primària Metropolitana Nord, Institut Català de Salut, Badalona, 08915, Spain
- Fundació Institut d’Investigació en ciències de la salut Germans Trias i Pujol (IGTP), Badalona, 08916, Spain
- Fundació UAB, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193, Spain
| | - Edurne Zabaleta-del-Olmo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Gerència Territorial de Barcelona, Institut Català de la Salut, Carrer de Balmes 22, Barcelona, 08007, Spain
- Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, 17003, Spain
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Ramon-Gonen R, Dori A, Shelly S. Towards a practical use of text mining approaches in electrodiagnostic data. Sci Rep 2023; 13:19483. [PMID: 37945618 PMCID: PMC10636146 DOI: 10.1038/s41598-023-45758-0] [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: 04/20/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
Healthcare professionals produce abounding textual data in their daily clinical practice. Text mining can yield valuable insights from unstructured data. Extracting insights from multiple information sources is a major challenge in computational medicine. In this study, our objective was to illustrate how combining text mining techniques with statistical methodologies can yield new insights and contribute to the development of neurological and neuromuscular-related health information. We demonstrate how to utilize and derive knowledge from medical text, identify patient groups with similar diagnostic attributes, and examine differences between groups using demographical data and past medical history (PMH). We conducted a retrospective study for all patients who underwent electrodiagnostic (EDX) evaluation in Israel's Sheba Medical Center between May 2016 and February 2022. The data extracted for each patient included demographic data, test results, and unstructured summary reports. We conducted several analyses, including topic modeling that targeted clinical impressions and topic analysis to reveal age- and sex-related differences. The use of suspected clinical condition text enriched the data and generated additional attributes used to find associations between patients' PMH and the emerging diagnosis topics. We identified 6096 abnormal EMG results, of which 58% (n = 3512) were males. Based on the latent Dirichlet allocation algorithm we identified 25 topics that represent different diagnoses. Sex-related differences emerged in 7 topics, 3 male-associated and 4 female-associated. Brachial plexopathy, myasthenia gravis, and NMJ Disorders showed statistically significant age and sex differences. We extracted keywords related to past medical history (n = 37) and tested them for association with the different topics. Several topics revealed a close association with past medical history, for example, length-dependent symmetric axonal polyneuropathy with diabetes mellitus (DM), length-dependent sensory polyneuropathy with chemotherapy treatments and DM, brachial plexopathy with motor vehicle accidents, myasthenia gravis and NMJ disorders with botulin treatments, and amyotrophic lateral sclerosis with swallowing difficulty. Summarizing visualizations were created to easily grasp the results and facilitate focusing on the main insights. In this study, we demonstrate the efficacy of utilizing advanced computational methods in a corpus of textual data to accelerate clinical research. Additionally, using these methods allows for generating clinical insights, which may aid in the development of a decision-making process in real-life clinical practice.
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Affiliation(s)
- Roni Ramon-Gonen
- The Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel.
| | - Amir Dori
- Department of Neurology, Sheba Medical Center, Tel HaShomer, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shahar Shelly
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
- Neuroimmunology Laboratory, The Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Tragaris T, Benetos IS, Vlamis J, Pneumaticos S. Machine Learning Applications in Spine Surgery. Cureus 2023; 15:e48078. [PMID: 38046496 PMCID: PMC10689893 DOI: 10.7759/cureus.48078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.
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Affiliation(s)
- Themistoklis Tragaris
- 1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Ioannis S Benetos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - John Vlamis
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Spyridon Pneumaticos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
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Khan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ. Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 2023; 44:947-959. [PMID: 37207359 PMCID: PMC10502574 DOI: 10.1210/endrev/bnad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/14/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.
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Affiliation(s)
- Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Stephanie E Baldeweg
- Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK
| | - Neil L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Digital Surgery Ltd, Medtronic, London WD18 8WW, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
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Li J, Huang Y, Hutton GJ, Aparasu RR. Assessing treatment switch among patients with multiple sclerosis: A machine learning approach. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 11:100307. [PMID: 37554927 PMCID: PMC10405092 DOI: 10.1016/j.rcsop.2023.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/08/2023] [Accepted: 07/09/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients. METHODS This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance. RESULTS In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index. CONCLUSIONS Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.
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Affiliation(s)
- Jieni Li
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
| | - Yinan Huang
- Department of Pharmacy Administration, College of Pharmacy, University of Mississippi, Oxford, MS, USA
| | | | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
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Kazemzadeh K, Akhlaghdoust M, Zali A. Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery. Front Surg 2023; 10:1241923. [PMID: 37693641 PMCID: PMC10483402 DOI: 10.3389/fsurg.2023.1241923] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.
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Affiliation(s)
- Kimia Kazemzadeh
- Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Meisam Akhlaghdoust
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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26
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Yu Y, Song G, Zhao Y, Liang J, Liu Q. Prediction of Vestibular Schwannoma Surgical Outcome Using Deep Neural Network. World Neurosurg 2023; 176:e60-e67. [PMID: 36966911 DOI: 10.1016/j.wneu.2023.03.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/21/2023] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To compare shallow machine learning models and deep neural network (DNN) model in prediction of vestibular schwannoma (VS) surgical outcome. METHODS One hundred eighty-eight patients with VS were included; all underwent suboccipital retrosigmoid sinus approach, and preoperative magnetic resonance imaging recorded a series of patient characteristics. Degree of tumor resection was collected during surgery, and facial nerve function was evaluated on the eighth day after surgery. Potential predictors of VS surgical outcome were obtained by univariate analysis, including tumor diameter, tumor volume, tumor surface area, brain tissue edema, tumor property, and tumor shape. This study proposes a DNN framework to predict the prognosis of VS surgical outcomes based on potential predictors and compares it with a series of classic machine learning algorithms including logistic regression. RESULTS The results showed that 3 predictors of tumor diameter, tumor volume, and tumor surface area were the most important prognostic factors for VS surgical outcomes, followed by tumor shape, while brain tissue edema and tumor property were the least influential. Different from shallow machine learning models, such as logistic regression with average performance (area under the curve: 0.8263; accuracy: 81.38%), the proposed DNN shows better performance, where area under the curve and accuracy were 0.8723 and 85.64%, respectively. CONCLUSIONS Based on potential risk factors, DNN can be exploited to achieve preoperative automatic assessment of VS surgical outcomes, and its performance is significantly better than other methods. It is therefore highly warranted to continue to investigate their utility as complementary clinical tools in predicting surgical outcomes preoperatively.
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Affiliation(s)
- Yansuo Yu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Gang Song
- Department of Neurosurgery, XuanWu Hospital, Capital Medical University, Beijing, China
| | - Yixin Zhao
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, XuanWu Hospital, Capital Medical University, Beijing, China.
| | - Qiang Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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Tang CHM, Seah JCY, Ahmad HK, Milne MR, Wardman JB, Buchlak QD, Esmaili N, Lambert JF, Jones CM. Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification. Diagnostics (Basel) 2023; 13:2317. [PMID: 37510062 PMCID: PMC10378683 DOI: 10.3390/diagnostics13142317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86-0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.
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Affiliation(s)
- Cyril H M Tang
- Annalise.ai, Sydney, NSW 2000, Australia
- Intensive Care Unit, Gosford Hospital, Sydney, NSW 2250, Australia
| | - Jarrel C Y Seah
- Annalise.ai, Sydney, NSW 2000, Australia
- Department of Radiology, Alfred Health, Melbourne, VIC 3004, Australia
| | | | | | | | - Quinlan D Buchlak
- Annalise.ai, Sydney, NSW 2000, Australia
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW 2007, Australia
- Department of Neurosurgery, Monash Health, Melbourne, VIC 3168, Australia
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW 2007, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | | | - Catherine M Jones
- Annalise.ai, Sydney, NSW 2000, Australia
- I-MED Radiology Network, Brisbane, QLD 4006, Australia
- School of Public and Preventive Health, Monash University, Clayton, VIC 3800, Australia
- Department of Clinical Imaging Science, University of Sydney, Sydney, NSW 2006, Australia
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Shahrestani S, Shlobin N, Gendreau JL, Brown NJ, Himstead A, Patel NA, Pierzchajlo N, Chakravarti S, Lee DJ, Chiarelli PA, Bullis CL, Chu J. Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning. Pediatr Neurosurg 2023; 58:206-214. [PMID: 37393891 PMCID: PMC10614444 DOI: 10.1159/000531754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/02/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Hydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD). METHODS The HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created. RESULTS A total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04-1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33-4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76-0.99) and elective admissions (OR: 0.62, 95% CI: 0.53-0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus. CONCLUSION Efficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.
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Affiliation(s)
- Shane Shahrestani
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA
| | - Nathan Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Julian L Gendreau
- Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA
| | - Nolan J Brown
- School of Medicine, University of California, Irvine, California, USA
| | - Alexander Himstead
- Department of Neurological Surgery, University of California, Irvine, California, USA
| | - Neal A Patel
- School of Medicine, Mercer University, Macon, Georgia, USA
| | | | - Sachiv Chakravarti
- Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland, USA
| | - Darrin Jason Lee
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Peter A Chiarelli
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Carli L Bullis
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Jason Chu
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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Srinivas S, Young AJ. Machine Learning and Artificial Intelligence in Surgical Research. Surg Clin North Am 2023; 103:299-316. [PMID: 36948720 DOI: 10.1016/j.suc.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care.
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Affiliation(s)
- Shruthi Srinivas
- Department of Surgery, The Ohio State University, 370 West 9th Avenue, Columbus, OH 43210, USA
| | - Andrew J Young
- Division of Trauma, Critical Care, and Burn, The Ohio State University, 181 Taylor Avenue, Suite 1102K, Columbus, OH 43203, USA.
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Nabukenya J, Drumright L, Alunyu AE, Semwanga AR. Critical risk and success factors for sustainability of an electronic health data capture, processing and dissemination platform for Uganda. Health Informatics J 2023; 29:14604582231180576. [PMID: 37256870 DOI: 10.1177/14604582231180576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Several studies have investigated challenges that have marred success or even caused the failure of eHealth implementations in Uganda; however, none has focused on the risks and success factors of their sustainability. This study explored critical risk and success factors for the sustainability of an electronic health data capture, processing and dissemination platform for Uganda. A mixed-method research design was followed involving collecting empirical data from all four regions of Uganda. A purposive sampling strategy was used to select the study districts per region, health facilities per district, and respondents/participants per facility or district. Findings revealed several risks and success factors for sustainability, including; bad leadership, corruption, lack of sustainable maintenance programs, lack of suitable sustainability plans, lack of ICT infrastructure investment, poor management systems, funds, stakeholder buy-ins, data sharing and access rights. The success factors included reinvestments as a partial sustainability plan for ICT infrastructure. These factors can be leveraged to ensure the continued operation of eHealth implementations in Uganda. Every electronic health project aiming at success should always make due consideration/sustainability plan at the onset of project conceptualisation; as lack of such a plan has often resulted in failed projects after the initial funds have been withdrawn.
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Affiliation(s)
- Josephine Nabukenya
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| | - Lydia Drumright
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Andrew Egwar Alunyu
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| | - Agnes Rwashana Semwanga
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
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Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study. Front Oncol 2023; 13:1046519. [PMID: 37035179 PMCID: PMC10076706 DOI: 10.3389/fonc.2023.1046519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 01/27/2023] [Indexed: 04/11/2023] Open
Abstract
Background Cerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, via a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis. Methods A dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided into two categories, preoperative risk factors; and repair techniques, with 6 and 11 variables respectively. Three types of machine learning models were developed in order to predict CSFR: logistic regression (LR); decision tree (DT); and neural network (NN). Models were validated using 5-fold cross-validation, compared via their area under the curve (AUC) evaluation metric, and key prediction variables were identified using their Shapley additive explanations (SHAP) score. Results CSFR rates were 3.9% (28/725) for the transsphenoidal approach and 7.1% (10/140) for the expanded endonasal approach. NNs outperformed LR and DT for CSFR prediction, with a mean AUC of 0.80 (0.70-0.90) for TSA and 0.78 (0.60-0.96) for EEA, when all risk factor and intraoperative repair data were integrated into the model. The presence of intraoperative CSF leak was the most prominent risk factor for CSFR. Elevated BMI and revision surgery were also associated with CSFR for the transsphenoidal approach. CSF diversion and gasket sealing appear to be strong predictors of the absence of CSFR for both approaches. Conclusion Neural networks are effective at predicting CSFR and uncovering key CSFR predictors in patients following endonasal skull base surgery, outperforming traditional statistical methods. These models will be improved further with larger and more granular datasets, improved NN architecture, and external validation. In the future, such predictive models could be used to assist surgical decision-making and support more individualised patient counselling.
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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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Ahmad HK, Milne MR, Buchlak QD, Ektas N, Sanderson G, Chamtie H, Karunasena S, Chiang J, Holt X, Tang CHM, Seah JCY, Bottrell G, Esmaili N, Brotchie P, Jones C. Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13040743. [PMID: 36832231 PMCID: PMC9955112 DOI: 10.3390/diagnostics13040743] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.
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Affiliation(s)
- Hassan K. Ahmad
- Annalise.ai, Sydney, NSW 2000, Australia
- Department of Emergency Medicine, Royal North Shore Hospital, Sydney, NSW 2065, Australia
- Correspondence:
| | | | - Quinlan D. Buchlak
- Annalise.ai, Sydney, NSW 2000, Australia
- School of Medicine, University of Notre Dame Australia, Sydney, NSW 2007, Australia
- Department of Neurosurgery, Monash Health, Melbourne, VIC 3168, Australia
| | | | | | | | | | - Jason Chiang
- Annalise.ai, Sydney, NSW 2000, Australia
- Department of General Practice, University of Melbourne, Melbourne, VIC 3010, Australia
- Westmead Applied Research Centre, University of Sydney, Sydney, NSW 2006, Australia
| | | | | | - Jarrel C. Y. Seah
- Annalise.ai, Sydney, NSW 2000, Australia
- Department of Radiology, Alfred Health, Melbourne, VIC 3004, Australia
| | | | - Nazanin Esmaili
- School of Medicine, University of Notre Dame Australia, Sydney, NSW 2007, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Peter Brotchie
- Annalise.ai, Sydney, NSW 2000, Australia
- Department of Radiology, St Vincent’s Health Australia, Melbourne, VIC 3065, Australia
| | - Catherine Jones
- Annalise.ai, Sydney, NSW 2000, Australia
- I-MED Radiology Network, Brisbane, QLD 4006, Australia
- School of Public and Preventive Health, Monash University, Clayton, VIC 3800, Australia
- Department of Clinical Imaging Science, University of Sydney, Sydney, NSW 2006, Australia
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Wang K, Zheng C, Xue L, Deng D, Zeng L, Li M, Deng X. A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years. Front Med (Lausanne) 2023; 10:999312. [PMID: 36844225 PMCID: PMC9945529 DOI: 10.3389/fmed.2023.999312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Background Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore the current research status and deficiencies from a macro perspective on TNBC publications. Methods PubMed publications under "triple-negative breast cancer" were searched and downloaded between January 2005 and 2022. R and Python extracted MeSH terms, geographic information, and other abstracts from metadata. The Latent Dirichlet Allocation (LDA) algorithm was applied to identify specific research topics. The Louvain algorithm established a topic network, identifying the topic's relationship. Results A total of 16,826 publications were identified, with an average annual growth rate of 74.7%. Ninety-eight countries and regions in the world participated in TNBC research. Molecular pathogenesis and medication are most studied in TNBC research. The publications mainly focused on three aspects: Therapeutic target research, Prognostic research, and Mechanism research. The algorithm and citation suggested that TNBC research is based on technology that advances TNBC subtyping, new drug development, and clinical trials. Conclusion This study quantitatively analyzes the current status of TNBC research from a macro perspective and will aid in redirecting basic and clinical research toward a better outcome for TNBC. Therapeutic target research and Nanoparticle research are the present research focus. There may be a lack of research on TNBC from a patient perspective, health economics, and end-of-life care perspectives. The research direction of TNBC may require the intervention of new technologies.
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Affiliation(s)
- Kangtao Wang
- Department of General Surgery, The Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chanjuan Zheng
- Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Lian Xue
- Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Dexin Deng
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Liang Zeng
- Department of Pathology, Guangzhou Women and Children’s Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China,*Correspondence: Liang Zeng,
| | - Ming Li
- Department of Immunology, College of Basic Medical Sciences, Central South University, Changsha, Hunan, China,Ming Li,
| | - Xiyun Deng
- Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China,Xiyun Deng,
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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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[Robotics and computer-assisted procedures in cranial neurosurgery]. CHIRURGIE (HEIDELBERG, GERMANY) 2023; 94:299-306. [PMID: 36629923 DOI: 10.1007/s00104-022-01783-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND The medical technical innovations over the last decade have made operations in the highly sensitive regions of the brain much safer. OBJECTIVE Presentation of how far computer assistance and robotics have become incorporated into clinical neurosurgery. MATERIAL AND METHOD Evaluation of the scientific literature and analysis of the certification status of the corresponding medical devices. RESULTS The rapid development of computer technology and the switch to digital imaging has led to the widespread introduction of neurosurgical planning software and intraoperative neuronavigation. In the field of robotics, the penetration into clinical neurosurgery is currently still largely limited to the automatic setting of trajectories. CONCLUSION The digitalization of imaging has fundamentally transformed neurosurgery. In the field of cranial neurosurgery, computer-assisted procedures can now be distinguished from noncomputer-assisted procedures only in a handful of cases. In the coming years important innovations for the clinical implementation can be expected in the field of robotics.
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Mosa DT, Mahmoud A, Zaki J, Sorour SE, El-Sappagh S, Abuhmed T. Henry gas solubility optimization double machine learning classifier for neurosurgical patients. PLoS One 2023; 18:e0285455. [PMID: 37167226 PMCID: PMC10174516 DOI: 10.1371/journal.pone.0285455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients' data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients' outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients' data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers.
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Affiliation(s)
- Diana T Mosa
- Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafr El-Shaikh, Egypt
| | - Amena Mahmoud
- Department of Computer Sciences, Faculty of Computers and Information, Kafrelsheikh University, Kafr El-Shaikh, Egypt
| | - John Zaki
- Department of Computer and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Shaymaa E Sorour
- Preparation- Computer Science and Education, Faculty of Specific Education, Kafrelsheikh University, Kafr El-Shaikh, Egypt
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
- Faculty of Computers & Artificial Intelligence, Benha University, Banha, Egypt
- College of computing and informatics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Tamer Abuhmed
- College of computing and informatics, Sungkyunkwan University, Seoul, Republic of Korea
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Miyagawa T, Saga M, Sasaki M, Shimizu M, Yamaura A. Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury. PLoS One 2023; 18:e0278562. [PMID: 36595496 PMCID: PMC9810188 DOI: 10.1371/journal.pone.0278562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/18/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Minor head trauma in children is a common reason for emergency department visits, but the risk of traumatic brain injury (TBI) in those children is very low. Therefore, physicians should consider the indication for computed tomography (CT) to avoid unnecessary radiation exposure to children. The purpose of this study was to statistically assess the differences between control and mild TBI (mTBI). In addition, we also investigate the feasibility of machine learning (ML) to predict the necessity of CT scans in children with mTBI. METHODS AND FINDINGS The study enrolled 1100 children under the age of 2 years to assess pre-verbal children. Other inclusion and exclusion criteria were per the PECARN study. Data such as demographics, injury details, medical history, and neurological assessment were used for statistical evaluation and creation of the ML algorithm. The number of children with clinically important TBI (ciTBI), mTBI on CT, and controls was 28, 30, and 1042, respectively. Statistical significance between the control group and clinically significant TBI requiring hospitalization (csTBI: ciTBI+mTBI on CT) was demonstrated for all nonparametric predictors except severity of the injury mechanism. The comparison between the three groups also showed significance for all predictors (p<0.05). This study showed that supervised ML for predicting the need for CT scan can be generated with 95% accuracy. It also revealed the significance of each predictor in the decision tree, especially the "days of life." CONCLUSIONS These results confirm the role and importance of each of the predictors mentioned in the PECARN study and show that ML could discriminate between children with csTBI and the control group.
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Affiliation(s)
- Tadashi Miyagawa
- Department of Pediatric Neurosurgery, Matsudo City General Hospital, Matsudo, Japan
- * E-mail:
| | - Marina Saga
- Department of Neurosurgery, Matsudo City General Hospital, Matsudo, Japan
| | - Minami Sasaki
- Department of Neurosurgery, Matsudo City General Hospital, Matsudo, Japan
| | - Miyuki Shimizu
- Department of Neurosurgery, Matsudo City General Hospital, Matsudo, Japan
| | - Akira Yamaura
- Department of Neurosurgery, Matsudo City General Hospital, Matsudo, Japan
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Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Sci Rep 2023; 6:e962. [PMID: 36589632 PMCID: PMC9795991 DOI: 10.1002/hsr2.962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 12/29/2022] Open
Abstract
Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.
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Affiliation(s)
- Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mitra Montazeri
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mohadeseh Montazeri
- Department of Computer, Faculty of FatimahKerman Branch Technical and Vocational UniversityKermanIran
| | - Ali Afraz
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
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The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res 2023; 93:405-412. [PMID: 36376506 PMCID: PMC9660024 DOI: 10.1038/s41390-022-02380-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 09/15/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.
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Hassan AM, Rajesh A, Asaad M, Jonas NA, Coert JH, Mehrara BJ, Butler CE. Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications. Am Surg 2023; 89:25-30. [PMID: 35562124 PMCID: PMC9653510 DOI: 10.1177/00031348221101488] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.
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Affiliation(s)
- Abbas M. Hassan
- Department of Plastic and Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Aashish Rajesh
- Department of Surgery, University of Texas Health Science Center, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nelson A. Jonas
- Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J. Henk. Coert
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Babak J. Mehrara
- Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charles E. Butler
- Department of Plastic and Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Anand A, Flores AR, McDonald MF, Gadot R, Xu DS, Ropper AE. A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems. J Neurosurg Spine 2022; 38:417-424. [PMID: 36681945 DOI: 10.3171/2022.11.spine221009] [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: 09/06/2022] [Accepted: 11/17/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Knowledge of the manufacturer of the previously implanted pedicle screw systems prior to revision spinal surgery may facilitate faster and safer surgery. Often, this information is unavailable because patients are referred by other centers or because of missing information in the patients' records. Recently, machine learning and computer vision have gained wider use in clinical applications. The authors propose a computer vision approach to classify posterior thoracolumbar instrumentation systems. METHODS Lateral and anteroposterior (AP) radiographs obtained in patients undergoing posterior thoracolumbar pedicle screw implantation for any indication at the authors' institution (2015-2021) were obtained. DICOM images were cropped to include both the pedicle screws and rods. Images were labeled with the manufacturer according to the operative record. Multiple feature detection methods were tested (SURF, MESR, and Minimum Eigenvalues); however, the bag-of-visual-words technique with KAZE feature detection was ultimately used to construct a computer vision support vector machine (SVM) classifier for lateral, AP, and fused lateral and AP images. Accuracy was tested using an 80%/20% training/testing pseudorandom split over 100 iterations. Using a reader study, the authors compared the model performance with the current practice of surgeons and manufacturer representatives identifying spinal hardware by visual inspection. RESULTS Among the three image types, 355 lateral, 379 AP, and 338 fused radiographs were obtained. The five pedicle screw implants included in this study were the Globus Medical Creo, Medtronic Solera, NuVasive Reline, Stryker Xia, and DePuy Expedium. When the two most common manufacturers used at the authors' institution were binarily classified (Globus Medical and Medtronic), the accuracy rates for lateral, AP, and fused images were 93.15% ± 4.06%, 88.98% ± 4.08%, and 91.08% ± 5.30%, respectively. Classification accuracy decreased by approximately 10% with each additional manufacturer added. The multilevel five-way classification accuracy rates for lateral, AP, and fused images were 64.27% ± 5.13%, 60.95% ± 5.52%, and 65.90% ± 5.14%, respectively. In the reader study, the model performed five-way classification on 100 test images with 79% accuracy in 14 seconds, compared with an average of 44% accuracy in 20 minutes for two surgeons and three manufacturer representatives. CONCLUSIONS The authors developed a KAZE feature detector with an SVM classifier that successfully identified posterior thoracolumbar hardware at five-level classification. The model performed more accurately and efficiently than the method currently used in clinical practice. The relative computational simplicity of this model, from input to output, may facilitate future prospective studies in the clinical setting.
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Affiliation(s)
- Adrish Anand
- 1Department of Neurosurgery, Baylor College of Medicine, Houston
| | - Alex R Flores
- 1Department of Neurosurgery, Baylor College of Medicine, Houston
| | - Malcolm F McDonald
- 1Department of Neurosurgery, Baylor College of Medicine, Houston.,2Medical Scientist Training Program, Baylor College of Medicine, Houston, Texas; and
| | - Ron Gadot
- 1Department of Neurosurgery, Baylor College of Medicine, Houston
| | - David S Xu
- 3Department of Neurosurgery, The Ohio State University School of Medicine, Columbus, Ohio
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Lam L, Lam A, Bacchi S, Abou-Hamden A. Neurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learning. Br J Neurosurg 2022:1-5. [PMID: 36458628 DOI: 10.1080/02688697.2022.2151565] [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: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/19/2022] [Indexed: 12/05/2022]
Abstract
INTRODUCTION Deep learning may be able to assist with the prediction of neurosurgical inpatient outcomes. The aims of this study were to investigate deep learning and transfer learning in the prediction of several inpatient outcomes including timing of discharge and discharge destination. METHOD Data were collected on consecutive neurosurgical admissions from existing databases over a 15-month period. Following pre-processing artificial neural networks were applied to admission notes and ward round notes to predict four inpatient outcomes. Models were developed on the training dataset, before being tested on a hold-out test dataset and a validation dataset. RESULTS 1341 individual admissions were included in the study. Using transfer learning and an artificial neural network an area under the receiver operator curve (AUC) of 0.81 and 0.80 on the derivation and validation datasets was able to be achieved for the prediction of discharge within the next 48 hours using daily ward round notes. This result is in comparison to an AUC of 0.71 and 0.68 using an artificial neural network without transfer learning for the same outcome. When the artificial neural network with transfer learning was applied to the other outcomes AUC of 0.72, 0.93 and 0.83 was achieved on the validation datasets for predicting discharge within the next 7 days, survival to discharge and discharge to home as a destination. CONCLUSIONS Deep learning may predict inpatient neurosurgery outcomes from free-text medical data. Recurrent predictions with ward round notes enable the use of information obtained throughout hospital admissions in these estimates.
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Affiliation(s)
- Lydia Lam
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Antoinette Lam
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Stephen Bacchi
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Amal Abou-Hamden
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
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45
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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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Kim K, Lee MK, Shin HK, Lee H, Kim B, Kang S. Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam. Front Public Health 2022; 10:1023098. [PMID: 36438286 PMCID: PMC9683382 DOI: 10.3389/fpubh.2022.1023098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
Introduction In this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting. Methods We selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model. Results We recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection. Conclusion Simplified artificial intelligence could be helpful in clinical decision support in settings with limited resources.
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Affiliation(s)
- Kwanghyun Kim
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea,Department of Public Health, Graduate School, Yonsei University, Seoul, South Korea,*Correspondence: Kwanghyun Kim
| | - Myung-ken Lee
- Graduate School of Public Health, Kosin University College of Medicine, Busan, South Korea
| | - Hyun Kyung Shin
- Acryl, Seoul, South Korea,FineHealthcare, Seoul, South Korea
| | | | | | - Sunjoo Kang
- Graduate School of Public Health, Yonsei University, Seoul, South Korea,Sunjoo Kang
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Juang WC, Hsu MH, Cai ZX, Chen CM. Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care. PLoS One 2022; 17:e0276501. [PMID: 36315554 PMCID: PMC9621444 DOI: 10.1371/journal.pone.0276501] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 10/08/2022] [Indexed: 11/06/2022] Open
Abstract
Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician’s decision in response of patient’s physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient’s comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.
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Affiliation(s)
- Wang-Chuan Juang
- Quality Management Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Business Management, National Sun Yat-sen University, Kaohsiung, Taiwan
- * E-mail: (WCJ); (CMC)
| | - Ming-Hsia Hsu
- Department of Information Management, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Information Management, National Sun Yat-sen University, Kaohsiung Taiwan
| | - Zheng-Xun Cai
- Department of Information Management, National Sun Yat-sen University, Kaohsiung Taiwan
| | - Chia-Mei Chen
- Department of Information Management, National Sun Yat-sen University, Kaohsiung Taiwan
- * E-mail: (WCJ); (CMC)
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48
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Baur D, Kroboth K, Heyde CE, Voelker A. Convolutional Neural Networks in Spinal Magnetic Resonance Imaging: A Systematic Review. World Neurosurg 2022; 166:60-70. [PMID: 35863650 DOI: 10.1016/j.wneu.2022.07.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CNNs in image segmentation and pathology detection in spine magnetic resonance imaging. METHODS For this systematic literature review, the authors performed a systematic initial search of the PubMed/Medline and Web of Science (Core collection) databases for eligible investigations. The authors limited the search to observational studies. Outcome parameters were analyzed according to the inclusion criteria and assigned to 3 groups: 1) segmentation of anatomical structures, 2) segmentation and evaluation of pathologic structures, and 3) specific implementation of CNNs. RESULTS Twenty-four retrospectively designed articles met the inclusion criteria. Publication dates ranged from 2017 to 2021. In total, 14,065 patients with 113,110 analyzed images were included. Most authors trained their network with a training-to-testing ratio of 80/20, while all but 2 articles used 5- to 10-fold cross-validation. Nine articles compared their performance results with other neural networks and algorithms, and all 24 articles described outcomes as positive. CONCLUSIONS State-of-the-art CNNs can detect and segment-specific anatomical landmarks and pathologies across a wide range, comparable to the skills of radiologists and experienced clinicians. With rapidly evolving network architectures and growing medical image databases, the future is likely to show growth in the development and refinement of these capable networks. However, the aid of automated segmentation and classification by neural networks cannot and should not be expected to replace clinical experts.
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Affiliation(s)
- David Baur
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Katharina Kroboth
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Christoph-Eckhard Heyde
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Anna Voelker
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
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Say I, Chen YE, Sun MZ, Li JJ, Lu DC. Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:1005168. [PMID: 36211830 PMCID: PMC9535093 DOI: 10.3389/fresc.2022.1005168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge.
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Affiliation(s)
- Irene Say
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | - Yiling Elaine Chen
- Department of Statistics, University of California, Los Angeles, CA, United States
| | - Matthew Z. Sun
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA, United States
| | - Daniel C. Lu
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
- Neuromotor Recovery and Rehabilitation Center, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
- Brain Research Institute, University of California, Los Angeles, CA, United States
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50
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Lopez CD, Boddapati V, Lombardi JM, Lee NJ, Mathew J, Danford NC, Iyer RR, Dyrszka MD, Sardar ZM, Lenke LG, Lehman RA. Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review. Global Spine J 2022; 12:1561-1572. [PMID: 35227128 PMCID: PMC9393994 DOI: 10.1177/21925682211049164] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES This current systematic review sought to identify and evaluate all current research-based spine surgery applications of AI/ML in optimizing preoperative patient selection, as well as predicting and managing postoperative outcomes and complications. METHODS A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA guidelines. RESULTS After application of inclusion and exclusion criteria, 41 studies were included in this review. Bayesian networks had the highest average AUC (.80), and neural networks had the best accuracy (83.0%), sensitivity (81.5%), and specificity (71.8%). Preoperative planning/cost prediction models (.89,82.2%) and discharge/length of stay models (.80,78.0%) each reported significantly higher average AUC and accuracy compared to readmissions/reoperation prediction models (.67,70.2%) (P < .001, P = .005, respectively). Model performance also significantly varied across postoperative management applications for average AUC and accuracy values (P < .001, P < .027, respectively). CONCLUSIONS Generally, authors of the reviewed studies concluded that AI/ML offers a potentially beneficial tool for providers to optimize patient care and improve cost-efficiency. More specifically, AI/ML models performed best, on average, when optimizing preoperative patient selection and planning and predicting costs, hospital discharge, and length of stay. However, models were not as accurate in predicting postoperative complications, adverse events, and readmissions and reoperations. An understanding of AI/ML-based applications is becoming increasingly important, particularly in spine surgery, as the volume of reported literature, technology accessibility, and clinical applications continue to rapidly expand.
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Affiliation(s)
- Cesar D. Lopez
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Venkat Boddapati
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA,Venkat Boddapati, MD, Columbia University Irving Medical Center, 622 W. 168th St., PH-11, New York, NY 10032, USA.
| | - Joseph M. Lombardi
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nathan J. Lee
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Justin Mathew
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nicholas C. Danford
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Rajiv R. Iyer
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Marc D. Dyrszka
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Zeeshan M. Sardar
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Lawrence G. Lenke
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Ronald A. Lehman
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
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