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Fields MW, Zaifman J, Malka MS, Lee NJ, Rymond CC, Simhon ME, Quan T, Roye BD, Vitale MG. Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis. Spine Deform 2024; 12:1477-1483. [PMID: 38702550 DOI: 10.1007/s43390-024-00889-w] [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: 10/28/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery. METHODS Children under 10 with EOS were chosen from the American College of Surgeon's NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS. RESULTS The study included 1587 patients, mostly young (average age: 6.94 ± 2.58 years), with 33.1% experiencing prolonged LOS (n = 526). Most patients were female (59.2%, n = 940), with an average BMI of 17.0 ± 8.7. Factors influencing LOS were operative time, age, BMI, ASA class, levels operated on, etiology, nutritional support, pulmonary and neurologic comorbidities. The gradient boosting model performed best with a test accuracy of 0.723, AUC of 0.630, and a Brier score of 0.189, leading to a patient-specific risk calculator for prolonged LOS. CONCLUSIONS Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.
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Affiliation(s)
- Michael W Fields
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Jay Zaifman
- Department of Orthopaedic Surgery, New York University Langone Health, New York, NY, USA
| | - Matan S Malka
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA.
| | - Nathan J Lee
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Christina C Rymond
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Matthew E Simhon
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Theodore Quan
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Benjamin D Roye
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA
| | - Michael G Vitale
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA
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Goldman SN, Hui AT, Choi S, Mbamalu EK, Tirabady P, Eleswarapu AS, Gomez JA, Alvandi LM, Fornari ED. Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence. Spine Deform 2024:10.1007/s43390-024-00940-w. [PMID: 39153073 DOI: 10.1007/s43390-024-00940-w] [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] [Received: 10/21/2023] [Accepted: 07/13/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS. METHODS This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS. RESULTS 40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%. CONCLUSION This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.
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Affiliation(s)
- Samuel N Goldman
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Aaron T Hui
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Sharlene Choi
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Emmanuel K Mbamalu
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Parsa Tirabady
- Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Ananth S Eleswarapu
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Jaime A Gomez
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Leila M Alvandi
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
| | - Eric D Fornari
- Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA
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Wang S, Zhang X, Zheng J, Chen G, Jiao G, Peng S. Integration of Spinal Musculoskeletal System Parameters for Predicting OVCF in the Elderly: A Comprehensive Predictive Model. Global Spine J 2024:21925682241274371. [PMID: 39133465 DOI: 10.1177/21925682241274371] [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: 08/13/2024] Open
Abstract
STUDY DESIGN Systematic literature review. OBJECTIVES To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes. METHODS A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model's performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation. RESULTS The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit. CONCLUSIONS This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.
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Affiliation(s)
- Song Wang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Dongguan Key Laboratory of Central Nervous System Injury and Repair, Department of Orthopedic Surgery, The Sixth Affiliated Hospital of Jinan University (Dongguan Eastern Central Hospital), Dongguan, China
| | - Xin Zhang
- Division of Spine Surgery, Department of Orthopaedic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Shenzhen Key Laboratory of Musculoskeletal Tissue Reconstruction and Function Restoration, Shenzhen, China
| | - Junyong Zheng
- Division of Spine Surgery, Department of Orthopaedic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Shenzhen Key Laboratory of Musculoskeletal Tissue Reconstruction and Function Restoration, Shenzhen, China
| | - Guoliang Chen
- Department of Orthopedic Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Dongguan Key Laboratory of Central Nervous System Injury and Repair, Department of Orthopedic Surgery, The Sixth Affiliated Hospital of Jinan University (Dongguan Eastern Central Hospital), Dongguan, China
| | - Genlong Jiao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Dongguan Key Laboratory of Central Nervous System Injury and Repair, Department of Orthopedic Surgery, The Sixth Affiliated Hospital of Jinan University (Dongguan Eastern Central Hospital), Dongguan, China
| | - Songlin Peng
- Division of Spine Surgery, Department of Orthopaedic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Shenzhen Key Laboratory of Musculoskeletal Tissue Reconstruction and Function Restoration, Shenzhen, China
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De Barros A, Abel F, Kolisnyk S, Geraci GC, Hill F, Engrav M, Samavedi S, Suldina O, Kim J, Rusakov A, Lebl DR, Mourad R. Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning. Global Spine J 2024; 14:1753-1759. [PMID: 36752058 PMCID: PMC11268295 DOI: 10.1177/21925682231155844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
STUDY DESIGN Medical vignettes. OBJECTIVES Lumbar spinal stenosis (LSS) is a degenerative condition with a high prevalence in the elderly population, that is associated with a significant economic burden and often requires spinal surgery. Prior authorization of surgical candidates is required before patients can be covered by a health plan and must be approved by medical directors (MDs), which is often subjective and clinician specific. In this study, we hypothesized that the prediction accuracy of machine learning (ML) methods regarding surgical candidates is comparable to that of a panel of MDs. METHODS Based on patient demographic factors, previous therapeutic history, symptoms and physical examinations and imaging findings, we propose an ML which computes the probability of spinal surgical recommendations for LSS. The model implements a random forest model trained from medical vignette data reviewed by MDs. Sets of 400 and 100 medical vignettes reviewed by MDs were used for training and testing. RESULTS The predictive accuracy of the machine learning model was with a root mean square error (RMSE) between model predictions and ground truth of .1123, while the average RMSE between individual MD's recommendations and ground truth was .2661. For binary classification, the AUROC and Cohen's kappa were .959 and .801, while the corresponding average metrics based on individual MD's recommendations were .844 and .564, respectively. CONCLUSIONS Our results suggest that ML can be used to automate prior authorization approval of surgery for LSS with performance comparable to a panel of MDs.
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Affiliation(s)
- Amaury De Barros
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, Toulouse, France
- Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France
| | | | | | | | | | | | | | | | | | | | | | - Raphael Mourad
- Remedy Logic, New York, NY, USA
- University of Toulouse, Toulouse, France
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Hornung AL, Rudisill SS, McCormick JR, Streepy JT, Harkin WE, Bryson N, Simcock X, Garrigues GE. Preoperative factors predict prolonged length of stay, serious adverse complications, and readmission following operative intervention of proximal humerus fractures: a machine learning analysis of a national database. JSES Int 2024; 8:699-708. [PMID: 39035667 PMCID: PMC11258835 DOI: 10.1016/j.jseint.2024.02.005] [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] [Indexed: 07/23/2024] Open
Abstract
Background Proximal humerus fractures are a common injury, predominantly affecting older adults. This study aimed to develop risk-prediction models for prolonged length of hospital stay (LOS), serious adverse complications, and readmission within 30 days of surgically treated proximal humerus fractures using machine learning (ML) techniques. Methods Adult patients (age >18) who underwent open reduction internal fixation (ORIF), hemiarthroplasty, or total shoulder arthroplasty for proximal humerus fracture between 2016 and 2021 were included. Preoperative demographic and clinical variables were collected for all patients and used to establish ML-based algorithms. The model with optimal performance was selected according to area under the curve (AUC) on the receiver operating curve (ROC) curve and overall accuracy, and the specific predictive features most important to model derivation were identified. Results A total of 7473 patients were included (72.1% male, mean age 66.2 ± 13.7 years). Models produced via gradient boosting performed best for predicting prolonged LOS and complications. The model predicting prolonged LOS demonstrated good discrimination and performance, as indicated by (Mean: 0.700, SE: 0.017), recall (Mean: 0.551, SE: 0.017), accuracy (Mean: 0.717, SE: 0.010), F1-score (Mean: 0.616, SE: 0.014), AUC (Mean: 0.779, SE: 0.010), and Brier score (Mean: 0.283, SE: 0.010) Preoperative hematocrit, preoperative platelet count, and patient age were considered the strongest predictive features. The model predicting serious adverse complications exhibited comparable discrimination [precision (Mean: 0.226, SE: 0.024), recall (Mean: 0.697, SE: 0.048), accuracy (Mean: 0.811, SE: 0.010), F1-score (Mean: 0.341, SE: 0.031)] and superior performance relative to the LOS model [AUC (Mean: 0.806, SE: 0.024), Brier score (Mean: 0.189, SE: 0.010), noting preoperative hematocrit, operative time, and patient age to be most influential. However, the 30-day readmission model achieved the weakest relative performance, displaying low measures of precision (Mean: 0.070, SE: 0.012) and recall (Mean: 0.389, SE: 0.053), despite good accuracy (Mean: 0.791, SE: 0.009). Conclusion Predictive models constructed using ML techniques demonstrated favorable discrimination and satisfactory-to-excellent performance in forecasting prolonged LOS and serious adverse complications occurring within 30 days of surgical intervention for proximal humerus fracture. Modifiable preoperative factors such as hematocrit and platelet count were identified as significant predictive features, suggesting that clinicians could address these factors during preoperative patient optimization to enhance outcomes. Overall, these findings highlight the potential for ML techniques to enhance preoperative management, facilitate shared decision-making, and enable more effective and personalized orthopedic care by exploring alternative approaches to risk stratification.
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Affiliation(s)
- Alexander L. Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | | | - John T. Streepy
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - William E. Harkin
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Noah Bryson
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Xavier Simcock
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Grant E. Garrigues
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
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Haas JW, Oakley PA, Betz JW, Miller JE, Jaeger JO, Moustafa IM, Harrison DE. Sagittal Full-Spine vs. Sectional Cervical Lateral Radiographs: Are the Measurements of Cervical Alignment Interchangeable? J Clin Med 2024; 13:2502. [PMID: 38731030 PMCID: PMC11084776 DOI: 10.3390/jcm13092502] [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: 02/14/2024] [Revised: 03/18/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: This study assessed the relationship between cervical spine parameters taken on standing full-spine lateral radiographic images compared to sectional lateral cervical radiographs. (2) Methods: Full-spine (FS) and sectional lateral cervical (LC) radiographs from four spine treatment facilities across the USA retrospectively provided data collected on 220 persons to assess the comparison of three sagittal cervical radiographic measurements between the two views. The measures included cervical lordosis using the absolute rotation angle from C2-C7, sagittal cervical translation of C2-C7, and atlas plane angle to horizontal. Linear correlation and R2 models were used for statistical comparison of the measures for the two views. (3) Results: The mean values of the three measurements were statistically different from each other: C2-C7 translation (FS = 19.84 ± 11.98 vs. LC = 21.18 ± 11.8), C2-C7 lordosis (FS = -15.3 ± 14.63 vs. LC = -18.32 ± 13.16), and atlas plane (FS = -19.99 ± 8.88 vs. LC = -22.56 ± 8.93), where all values were p < 0.001. Weak-to-moderate-to-strong correlations existed between the full-spine and sectional lateral cervical radiographic variables. The R2 values varied based on the measurement were R2 = 0.768 (p < 0.001) for sagittal cervical translation of C2-C7 (strong), R2 = 0.613 (p < 0.001) for the absolute rotation angle C2-C7 (moderate), and R2 = 0.406 (p < 0.001) for the atlas plane line (weak). Though a linear correlation was identified, there were consistent intra-person differences between the measurements on the full spine versus sectional lateral cervical radiographic views, where the full-spine view consistently underestimated the magnitude of the variables. (4) Conclusion: Key sagittal cervical radiographic measurements on the full spine versus sectional lateral cervical radiographic views show striking intra-person differences. The findings of this study confirm that full spine versus sectional lateral cervical radiographic views provide different biomechanical magnitudes of cervical sagittal alignment, and caution should be exercised by health care providers as these are not interchangeable. We recommend the LC view for measurement of cervical sagittal alignment variables.
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Affiliation(s)
- Jason W. Haas
- CBP NonProfit, Inc., Eagle, ID 83616, USA; (J.W.H.); (J.W.B.); (J.E.M.); (J.O.J.)
| | - Paul A. Oakley
- Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, Canada;
| | - Joseph W. Betz
- CBP NonProfit, Inc., Eagle, ID 83616, USA; (J.W.H.); (J.W.B.); (J.E.M.); (J.O.J.)
- Private Practice, Boise, ID 83709, USA
| | - Jason E. Miller
- CBP NonProfit, Inc., Eagle, ID 83616, USA; (J.W.H.); (J.W.B.); (J.E.M.); (J.O.J.)
- Private Practice, Lakewood, CO 80226, USA
| | - Jason O. Jaeger
- CBP NonProfit, Inc., Eagle, ID 83616, USA; (J.W.H.); (J.W.B.); (J.E.M.); (J.O.J.)
- Community Based Internship Program, Associate Faculty, Southern California University of Health Sciences, Whittier, CA 90604, USA
| | - Ibrahim M. Moustafa
- Department of Physiotherapy, College of Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Neuromusculoskeletal Rehabilitation Research Group, RIMHS–Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Deed E. Harrison
- CBP NonProfit, Inc., Eagle, ID 83616, USA; (J.W.H.); (J.W.B.); (J.E.M.); (J.O.J.)
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Lang S, Vitale J, Fekete TF, Haschtmann D, Reitmeir R, Ropelato M, Puhakka J, Galbusera F, Loibl M. Are large language models valid tools for patient information on lumbar disc herniation? The spine surgeons' perspective. BRAIN & SPINE 2024; 4:102804. [PMID: 38706800 PMCID: PMC11067000 DOI: 10.1016/j.bas.2024.102804] [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: 12/27/2023] [Revised: 02/19/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024]
Abstract
Introduction Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Material and methods Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from "excellent" to "unsatisfactory," and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Results Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. Discussion and conclusion LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication.
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Affiliation(s)
- Siegmund Lang
- Department of Trauma Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Jacopo Vitale
- Spine Center, Schulthess Klinik, Zurich, Switzerland
| | | | | | | | | | - Jani Puhakka
- Spine Center, Schulthess Klinik, Zurich, Switzerland
| | | | - Markus Loibl
- Spine Center, Schulthess Klinik, Zurich, Switzerland
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Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth 2024; 18:249-256. [PMID: 38654854 PMCID: PMC11033896 DOI: 10.4103/sja.sja_955_23] [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: 12/12/2023] [Revised: 12/18/2023] [Accepted: 12/26/2023] [Indexed: 04/26/2024] Open
Abstract
This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.
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Affiliation(s)
- Monika Kambale
- Symbiosis Institute of Health Sciences, Pune, Maharashtra, India
| | - Sammita Jadhav
- Symbiosis Institute of Health Sciences, Pune, Maharashtra, India
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Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res 2024; 19:191. [PMID: 38500199 PMCID: PMC10949620 DOI: 10.1186/s13018-024-04666-3] [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: 01/23/2024] [Accepted: 03/06/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Pelvic fragility fractures in elderly individuals present significant challenges in orthopedic and geriatric medicine due to reduced bone density and increased frailty associated with aging. METHODS This study involved 150 elderly patients with pelvic fragility fractures. The patients were divided into two groups, the observation group (Observation) and the control group (Control), using a random number table. Artificial intelligence, specifically the Tianji Orthopedic Robot, was employed for surgical assistance. The observation group received bone cement reinforcement along with screw fixation using the robotic system, while the control group received conventional screw fixation alone. Follow-up data were collected for one-year post-treatment. RESULTS The observation group exhibited significantly lower clinical healing time of fractures and reduced bed rest time compared to the control group. Additionally, the observation group experienced less postoperative pain at 1 and 3 months, indicating the benefits of bone cement reinforcement. Moreover, patients in the observation group demonstrated significantly better functional recovery at 1-, 3-, and 6-months post-surgery compared to the control group. CONCLUSION The combination of bone cement reinforcement and robotic technology resulted in accelerated fracture healing, reduced bed rest time, and improved postoperative pain relief and functional recovery.
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Affiliation(s)
- Lecai Gao
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China
| | - Baorui Xing
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China.
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Vadalà G, Ambrosio L, Denaro V. Safety and Complications Related to Emerging Technologies. Neurospine 2024; 21:6-7. [PMID: 38569626 PMCID: PMC10992665 DOI: 10.14245/ns.2448162.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024] Open
Affiliation(s)
- Gianluca Vadalà
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Luca Ambrosio
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Vincenzo Denaro
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
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Liawrungrueang W, Cho ST, Sarasombath P, Kim I, Kim JH. Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review. Asian Spine J 2024; 18:146-157. [PMID: 38130042 PMCID: PMC10910143 DOI: 10.31616/asj.2023.0410] [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] [Received: 12/09/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 12/23/2023] Open
Abstract
This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.
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Affiliation(s)
| | - Sung Tan Cho
- Department of Orthopaedics, Inje University Ilsan Paik Hospital, Goyang,
Korea
| | - Peem Sarasombath
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai,
Thailand
| | - Inhee Kim
- Department of Orthopaedics, Police National Hospital, Seoul,
Korea
| | - Jin Hwan Kim
- Department of Orthopaedics, Inje University Ilsan Paik Hospital, Goyang,
Korea
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12
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Maki S, Furuya T, Inoue M, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis. J Clin Med 2024; 13:705. [PMID: 38337399 PMCID: PMC10856760 DOI: 10.3390/jcm13030705] [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: 11/13/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Masahiro Inoue
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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13
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Gebauer GP. CORR Insights®: Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm. Clin Orthop Relat Res 2024; 482:158-160. [PMID: 37493449 PMCID: PMC10723897 DOI: 10.1097/corr.0000000000002782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Affiliation(s)
- Gregory P. Gebauer
- Orthopaedic Spine Surgeon, Advanced Orthopedic Center, Port Charlotte, FL, USA
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14
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Zhang Y, Hu M, Zhao W, Liu X, Peng Q, Meng B, Yang S, Feng X, Zhang L. A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care. J Neurol Surg A Cent Eur Neurosurg 2024; 85:62-73. [PMID: 36640757 DOI: 10.1055/a-2013-3149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders. METHODS On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping. RESULTS A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. CONCLUSIONS AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.
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Affiliation(s)
- Yu Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Man Hu
- Graduate School of Dalian Medical University, Dalian, China
| | - Wenjie Zhao
- Graduate School of Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Qing Peng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Bo Meng
- Graduate School of Dalian Medical University, Dalian, China
| | - Sheng Yang
- Graduate School of Dalian Medical University, Dalian, China
| | - Xinmin Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
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15
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Hallinan JTPD, Zhu L, Tan HWN, Hui SJ, Lim X, Ong BWL, Ong HY, Eide SE, Cheng AJL, Ge S, Kuah T, Lim SWD, Low XZ, Teo EC, Yap QV, Chan YH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Tan JH. A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3815-3824. [PMID: 37093263 DOI: 10.1007/s00586-023-07706-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/12/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore.
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Hui Wen Natalie Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Si Jian Hui
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Xinyi Lim
- Orthopaedic Centre, Alexandra Hospital, 378 Alexandra Road, Singapore, 159964, Singapore
| | - Bryan Wei Loong Ong
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Amanda J L Cheng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Shi Wei Desmond Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Naresh Kumar
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Jiong Hao Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
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16
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Koike Y, Yui M, Nakamura S, Yoshida A, Takegawa H, Anetai Y, Hirota K, Tanigawa N. Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans. Int J Comput Assist Radiol Surg 2023; 18:1867-1874. [PMID: 36991276 DOI: 10.1007/s11548-023-02880-8] [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: 09/19/2022] [Accepted: 03/17/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system. METHODS We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation. RESULTS The computation time was 0.44 s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions. CONCLUSION Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.
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Affiliation(s)
- Yuhei Koike
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan.
| | - Midori Yui
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Satoaki Nakamura
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Asami Yoshida
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Hideki Takegawa
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Yusuke Anetai
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Kazuki Hirota
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - Noboru Tanigawa
- Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
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17
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Adams MCB, Nelson AM, Narouze S. Daring discourse: artificial intelligence in pain medicine, opportunities and challenges. Reg Anesth Pain Med 2023; 48:439-442. [PMID: 37169486 PMCID: PMC10525018 DOI: 10.1136/rapm-2023-104526] [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: 03/18/2023] [Accepted: 04/28/2023] [Indexed: 05/13/2023]
Abstract
Artificial intelligence (AI) tools are currently expanding their influence within healthcare. For pain clinics, unfettered introduction of AI may cause concern in both patients and healthcare teams. Much of the concern stems from the lack of community standards and understanding of how the tools and algorithms function. Data literacy and understanding can be challenging even for experienced healthcare providers as these topics are not incorporated into standard clinical education pathways. Another reasonable concern involves the potential for encoding bias in healthcare screening and treatment using faulty algorithms. And yet, the massive volume of data generated by healthcare encounters is increasingly challenging for healthcare teams to navigate and will require an intervention to make the medical record manageable in the future. AI approaches that lighten the workload and support clinical decision-making may provide a solution to the ever-increasing menial tasks involved in clinical care. The potential for pain providers to have higher-quality connections with their patients and manage multiple complex data sources might balance the understandable concerns around data quality and decision-making that accompany introduction of AI. As a specialty, pain medicine will need to establish thoughtful and intentionally integrated AI tools to help clinicians navigate the changing landscape of patient care.
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Affiliation(s)
- Meredith C B Adams
- Departments of Anesthesiology, Biomedical Informatics, Physiology & Pharmacology, and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Ariana M Nelson
- Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California, USA
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18
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Shen J, Nemani VM, Leveque JC, Sethi R. Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery. J Am Acad Orthop Surg 2023; 31:901-907. [PMID: 37040614 DOI: 10.5435/jaaos-d-22-00789] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 03/01/2023] [Indexed: 04/13/2023] Open
Abstract
Personalized medicine has made a tremendous impact on patient care. Although initially, it revolutionized pharmaceutical development and targeted therapies in oncology, it has also made an important impact in orthopaedic surgery. The field of spine surgery highlights the effect of personalized medicine because the improved understanding of spinal pathologies and technological innovations has made personalized medicine a key component of patient care. There is evidence for several of these advancements to support their usage in improving patient care. Proper understanding of normative spinal alignment and surgical planning software has enabled surgeons to predict postoperative alignment accurately. Furthermore, 3D printing technologies have demonstrated the ability to improve pedicle screw placement accuracy compared with free-hand techniques. Patient-specific, precontoured rods have shown improved biomechanical properties, which reduces the risk of postoperative rod fractures. Moreover, approaches such as multidisciplinary evaluations tailored to specific patient needs have demonstrated the ability to decrease complications. Personalized medicine has shown the ability to improve care in all phases of surgical management, and several of these approaches are now readily available to orthopaedic surgeons.
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Affiliation(s)
- Jesse Shen
- From the Department of Orthopedic Surgery, Université de Montréal (Shen), the Virginia Mason Medical Center (Nemani, Leveque, and Sethi), University of Washington (Sethi)
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19
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Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [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: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
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Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
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20
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Saeed MU, Dikaios N, Dastgir A, Ali G, Hamid M, Hajjej F. An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images. Diagnostics (Basel) 2023; 13:2658. [PMID: 37627917 PMCID: PMC10453471 DOI: 10.3390/diagnostics13162658] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/09/2023] [Accepted: 04/20/2023] [Indexed: 08/27/2023] Open
Abstract
Spine image analysis is based on the accurate segmentation and vertebrae recognition of the spine. Several deep learning models have been proposed for spine segmentation and vertebrae recognition, but they are very computationally demanding. In this research, a novel deep learning model is introduced for spine segmentation and vertebrae recognition using CT images. The proposed model works in two steps: (1) A cascaded hierarchical atrous spatial pyramid pooling residual attention U-Net (CHASPPRAU-Net), which is a modified version of U-Net, is used for the segmentation of the spine. Cascaded spatial pyramid pooling layers, along with residual blocks, are used for feature extraction, while the attention module is used for focusing on regions of interest. (2) A 3D mobile residual U-Net (MRU-Net) is used for vertebrae recognition. MobileNetv2 includes residual and attention modules to accurately extract features from the axial, sagittal, and coronal views of 3D spine images. The features from these three views are concatenated to form a 3D feature map. After that, a 3D deep learning model is used for vertebrae recognition. The VerSe 20 and VerSe 19 datasets were used to validate the proposed model. The model achieved more accurate results in spine segmentation and vertebrae recognition than the state-of-the-art methods.
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Affiliation(s)
- Muhammad Usman Saeed
- Department of Computer Science, University of Okara, Okara 56310, Pakistan; (A.D.); (G.A.)
| | - Nikolaos Dikaios
- Mathematics Research Centre, Academy of Athens, 10679 Athens, Greece
| | - Aqsa Dastgir
- Department of Computer Science, University of Okara, Okara 56310, Pakistan; (A.D.); (G.A.)
| | - Ghulam Ali
- Department of Computer Science, University of Okara, Okara 56310, Pakistan; (A.D.); (G.A.)
| | - Muhammad Hamid
- Department of Computer Science, Government College Women University, Sialkot 51310, Pakistan;
| | - Fahima Hajjej
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
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21
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Helgeson MD, Pisano AJ, Fredericks DR, Wagner SC. What's New in Spine Surgery. J Bone Joint Surg Am 2023:00004623-990000000-00792. [PMID: 37141447 DOI: 10.2106/jbjs.23.00125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Affiliation(s)
- Melvin D Helgeson
- Department of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, Maryland
- Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Alfred J Pisano
- Department of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, Maryland
- Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Donald R Fredericks
- Department of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, Maryland
- Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Scott C Wagner
- Department of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, Maryland
- Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland
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22
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Rudisill SS, Hornung AL, Barajas JN, Bridge JJ, Mallow GM, Lopez W, Sayari AJ, Louie PK, Harada GK, Tao Y, Wilke HJ, Colman MW, Phillips FM, An HS, Samartzis D. Answer to the letter to the editor by Zhi-Hui Dai concerning "Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion" by Rudisill SS et al. (Eur Spine J [2022]; doi: 10.1007/s00586-022-07238-3). EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:3161-3162. [PMID: 36028590 DOI: 10.1007/s00586-022-07357-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Samuel S Rudisill
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Jack J Bridge
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- Department of Data Science and Analytics, University of Missouri, Colombia, MO, USA
| | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Wylie Lopez
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Philip K Louie
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
| | - Garrett K Harada
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Youping Tao
- Institute of Orthopaedic Research and Biomechanics, Ulm University Medical Centre, Ulm, Germany
| | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Ulm University Medical Centre, Ulm, Germany
| | - Matthew W Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60642, USA.
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA.
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23
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De la Garza Ramos R. Can We Make Spine Surgery Safer and Better? J Clin Med 2022; 11:jcm11123400. [PMID: 35743470 PMCID: PMC9225388 DOI: 10.3390/jcm11123400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 06/11/2022] [Indexed: 12/04/2022] Open
Affiliation(s)
- Rafael De la Garza Ramos
- Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10467, USA
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