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Mazzolenis MV, Mourra GN, Moreau S, Mazzolenis ME, Cerda IH, Vega J, Khan JS, Thérond A. The Role of Virtual Reality and Artificial Intelligence in Cognitive Pain Therapy: A Narrative Review. Curr Pain Headache Rep 2024:10.1007/s11916-024-01270-2. [PMID: 38850490 DOI: 10.1007/s11916-024-01270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2024] [Indexed: 06/10/2024]
Abstract
PURPOSE OF REVIEW This review investigates the roles of artificial intelligence (AI) and virtual reality (VR) in enhancing cognitive pain therapy for chronic pain management. The work assesses current research, outlines benefits and limitations and examines their potential integration into existing pain management methods. RECENT FINDINGS Advances in VR have shown promise in chronic pain management through immersive cognitive therapy exercises, with evidence supporting VR's effectiveness in symptom reduction. AI's personalization of treatment plans and its support for mental health through AI-driven avatars are emerging trends. The integration of AI in hybrid programs indicates a future with real-time adaptive technology tailored to individual needs in chronic pain management. Incorporating AI and VR into chronic pain cognitive therapy represents a promising approach to enhance management by leveraging VR's immersive experiences and AI's personalized tactics, aiming to improve patient engagement and outcomes. Nonetheless, further empirical studies are needed to standardized methodologies, compare these technologies to traditional therapies and fully realize their clinical potential.
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Affiliation(s)
| | - Gabrielle Naime Mourra
- Department of Marketing, Haute Ecole de Commerce Montreal, Montreal, QC, H2X 3P2, Canada
| | - Sacha Moreau
- Massachusetts Institute of Technology, Boston, MA, USA
| | - Maria Emilia Mazzolenis
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | | | - Julio Vega
- Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - James S Khan
- University of California, San Francisco, CA, USA
| | - Alexandra Thérond
- Department of Psychology, Université du Québec À Montréal, 100 Sherbrooke St W, Montréal, QC, Canada.
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Cerda IH, Zhang E, Dominguez M, Ahmed M, Lang M, Ashina S, Schatman ME, Yong RJ, Fonseca ACG. Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management. Curr Pain Headache Rep 2024:10.1007/s11916-024-01279-7. [PMID: 38836996 DOI: 10.1007/s11916-024-01279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. RECENT FINDINGS Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.
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Affiliation(s)
| | - Emily Zhang
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Moises Dominguez
- Department of Neurology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA
| | | | - Min Lang
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sait Ashina
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Schatman
- Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Department of Population Health-Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA
| | - R Jason Yong
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA
| | - Alexandra C G Fonseca
- Harvard Medical School, Boston, MA, USA.
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA.
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El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, Varrassi G, Nagiub MS. Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review. Pain Ther 2024; 13:293-317. [PMID: 38430433 PMCID: PMC11111436 DOI: 10.1007/s40122-024-00584-8] [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: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.
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Affiliation(s)
- Salah N El-Tallawy
- Anesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia.
- Anesthesia and Pain Department, Faculty of Medicine, Minia University & NCI, Cairo University, Giza, Egypt.
| | | | - Ingrid Vasiliu-Feltes
- Science, Entrepreneurship and Investments Institute, University of Miami, Miami, USA
| | - Rania S Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Huo J, Yu Y, Lin W, Hu A, Wu C. Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e51250. [PMID: 38607660 PMCID: PMC11053395 DOI: 10.2196/51250] [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: 07/26/2023] [Revised: 10/08/2023] [Accepted: 02/28/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. OBJECTIVE The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. METHODS The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. RESULTS A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. CONCLUSIONS This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. TRIAL REGISTRATION PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.
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Affiliation(s)
- Jian Huo
- Boston Intelligent Medical Research Center, Shenzhen United Scheme Technology Company Limited, Boston, MA, United States
| | - Yan Yu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
| | - Wei Lin
- Shenzhen United Scheme Technology Company Limited, Shenzhen, China
| | - Anmin Hu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
- Shenzhen United Scheme Technology Company Limited, Shenzhen, China
- The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Chaoran Wu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
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Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, Moreno Franco P, Diedrich DA, Pickering BW, Herasevich V. Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings. J Imaging 2024; 10:81. [PMID: 38667979 PMCID: PMC11050909 DOI: 10.3390/jimaging10040081] [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: 01/31/2024] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/28/2024] Open
Abstract
Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.
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Affiliation(s)
- Heidi Lindroth
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Center for Aging Research, Regenstrief Institute, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Health Innovation and Implementation Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Keivan Nalaie
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Roshini Raghu
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Ivan N. Ayala
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Charles Busch
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- College of Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Pablo Moreno Franco
- Department of Transplantation Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Daniel A. Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Brian W. Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
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Rozman de Moraes A, Erdogan E, Azhar A, Reddy SK, Lu Z, Geller JA, Graves DM, Kubiak MJ, Williams JL, Wu J, Bruera E, Yennurajalingam S. Scheduled and Breakthrough Opioid Use for Cancer Pain in an Inpatient Setting at a Tertiary Cancer Hospital. Curr Oncol 2024; 31:1335-1347. [PMID: 38534934 PMCID: PMC10969060 DOI: 10.3390/curroncol31030101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 05/26/2024] Open
Abstract
Background: Our aim was to examine the frequency and prescription pattern of breakthrough (BTO) and scheduled (SCH) opioids and their ratio (BTO/SCH ratio) of use, prior to and after referral to an inpatient supportive care consult (SCC) for cancer pain management (CPM). Methods and Materials: Patients admitted at the MD Anderson Cancer Center and referred to a SCC were retrospectively reviewed. Cancer patients receiving SCH and BTO opioids for ≥24 h were eligible for inclusion. Patient demographics and clinical characteristics, including the type and route of SCH and BTO opioids, daily opioid doses (MEDDs) of SCH and BTO, and BTO/SCH ratios were reviewed in patients seen prior to a SCC (pre-SCC) and during a SCC. A normal BTO ratio was defined as 0.5-0.2. Results: A total of 665/728 (91%) patients were evaluable. Median pain scores (p < 0.001), BTO MEDDs (p < 0.001), scheduled opioid MEDDs (p < 0.0001), and total MEDDs (p < 0.0001) were higher, but the median number of BTO doses was fewer (2 vs. 4, p < 0.001), among patients seen at SCC compared to pre-SCC. A BTO/SCH ratio over the recommended ratio (>0.2) was seen in 37.5% of patients. The BTO/SCH ratios in the pre-SCC and SCC groups were 0.10 (0.04, 0.21) and 0.17 (0.10, 0.30), respectively, p < 0.001. Hydromorphone and Morphine were the most common BTO and SCH opioids prescribed, respectively. Patients in the early supportive care group had higher pain scores and MEDDs. Conclusions: BTO/SCH ratios are frequently prescribed higher than the recommended dose. Daily pain scores, BTO MEDDs, scheduled opioid MEDDs, and total MEDDs were higher among the SCC group than the pre-SCC group, but the number of BTO doses/day was lower.
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Affiliation(s)
- Aline Rozman de Moraes
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Elif Erdogan
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Ahsan Azhar
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Suresh K. Reddy
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Zhanni Lu
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Joshua A. Geller
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - David Mill Graves
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Michal J. Kubiak
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Janet L. Williams
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Jimin Wu
- Department of Biostatistics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eduardo Bruera
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
| | - Sriram Yennurajalingam
- Department of Palliative Care, Rehabilitation Medicine, and Integrative Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; (A.R.d.M.); (E.E.)
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Ezell JM, Ajayi BP, Parikh T, Miller K, Rains A, Scales D. Drug Use and Artificial Intelligence: Weighing Concerns and Possibilities for Prevention. Am J Prev Med 2024; 66:568-572. [PMID: 38056683 DOI: 10.1016/j.amepre.2023.11.024] [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: 09/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/08/2023]
Affiliation(s)
- Jerel M Ezell
- Community Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California; Berkeley Center for Cultural Humility, University of California Berkeley, Berkeley, California.
| | - Babatunde Patrick Ajayi
- Community Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
| | - Tapan Parikh
- Information Science, The College of Arts & Sciences, Cornell University, New York, New York
| | - Kyle Miller
- Department of Medicine, Southern Illinois University, Carbondale, Illinois
| | - Alex Rains
- Pritzer School of Medicine, The University of Chicago, Chicago, Illinois
| | - David Scales
- Division of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, New York
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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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9
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Shrivastava M, Ye L. Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review. Int J Oral Sci 2023; 15:58. [PMID: 38155153 PMCID: PMC10754947 DOI: 10.1038/s41368-023-00254-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 12/30/2023] Open
Abstract
Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.
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Affiliation(s)
- Mayank Shrivastava
- Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - Liang Ye
- Department of Rehabilitation Medicine, University of Minnesota Medical School, Minneapolis, MN, USA.
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10
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Steagall PV, Monteiro BP, Marangoni S, Moussa M, Sautié M. Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale. Sci Rep 2023; 13:21584. [PMID: 38062194 PMCID: PMC10703818 DOI: 10.1038/s41598-023-49031-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023] Open
Abstract
This study used deep neural networks and machine learning models to predict facial landmark positions and pain scores using the Feline Grimace Scale© (FGS). A total of 3447 face images of cats were annotated with 37 landmarks. Convolutional neural networks (CNN) were trained and selected according to size, prediction time, predictive performance (normalized root mean squared error, NRMSE) and suitability for smartphone technology. Geometric descriptors (n = 35) were computed. XGBoost models were trained and selected according to predictive performance (accuracy; mean square error, MSE). For prediction of facial landmarks, the best CNN model had NRMSE of 16.76% (ShuffleNetV2). For prediction of FGS scores, the best XGBoost model had accuracy of 95.5% and MSE of 0.0096. Models showed excellent predictive performance and accuracy to discriminate painful and non-painful cats. This technology can now be used for the development of an automated, smartphone application for acute pain assessment in cats.
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Affiliation(s)
- P V Steagall
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada.
- Department of Veterinary Clinical Sciences and Centre for Animal Health and Welfare, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.
| | - B P Monteiro
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - S Marangoni
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - M Moussa
- Plateforme IA-Agrosanté, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - M Sautié
- Plateforme IA-Agrosanté, Université de Montréal, Saint-Hyacinthe, QC, Canada
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11
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Mocumbi AO, Agyepong IA, Kyobutungi C. Building on current progress to shape the future of biomedical science. Lancet 2023; 402:1204-1206. [PMID: 37805195 DOI: 10.1016/s0140-6736(23)01670-7] [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: 07/05/2023] [Accepted: 08/07/2023] [Indexed: 10/09/2023]
Affiliation(s)
- Ana Olga Mocumbi
- Universidade Eduardo Mondlane, Campus Universitário, 1102 Maputo, Moçambique.
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12
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Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus 2023; 15:e45038. [PMID: 37829964 PMCID: PMC10566398 DOI: 10.7759/cureus.45038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Abstract
In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.
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Affiliation(s)
- Meghna Singhal
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Lalit Gupta
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Kshitiz Hirani
- Department of Anesthesiology and Critical Care, University College of Medical Sciences and Guru Teg Bahadur Hospital, Delhi, IND
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Bakken S. Innovative informatics interventions to improve health and health care. J Am Med Inform Assoc 2023; 30:409-410. [PMID: 36794710 PMCID: PMC9933056 DOI: 10.1093/jamia/ocac255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 02/17/2023] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, New York, USA
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