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Ihara K, Dumkrieger G, Zhang P, Takizawa T, Schwedt TJ, Chiang CC. Application of Artificial Intelligence in the Headache Field. Curr Pain Headache Rep 2024:10.1007/s11916-024-01297-5. [PMID: 38976174 DOI: 10.1007/s11916-024-01297-5] [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: 06/27/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE OF REVIEW Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.
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Affiliation(s)
- Keiko Ihara
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
- Japanese Red Cross Ashikaga Hospital, Ashikaga, Tochigi, Japan
| | | | - Pengfei Zhang
- Department of Neurology, Rutgers University, New Brunswick, NJ, USA
| | - Tsubasa Takizawa
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Todd J Schwedt
- Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
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Okada M, Katsuki M, Shimazu T, Takeshima T, Mitsufuji T, Ito Y, Ohbayashi K, Imai N, Miyahara J, Matsumori Y, Nakazato Y, Fujita K, Hoshino E, Yamamoto T. Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study. Life (Basel) 2024; 14:744. [PMID: 38929727 PMCID: PMC11204521 DOI: 10.3390/life14060744] [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: 05/07/2024] [Revised: 06/05/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21-1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings.
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Affiliation(s)
- Mariko Okada
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Masahito Katsuki
- Physical Education and Health Center, Nagaoka University of Technology, Niigata 940-2137, Japan
| | - Tomokazu Shimazu
- Department of Neurology, Saitama Neuropsychiatric Institute, Saitama 338-8577, Japan
| | - Takao Takeshima
- Headache Center and Department of Neurology, Tominaga Hospital, Osaka 556-0017, Japan
| | - Takashi Mitsufuji
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Yasuo Ito
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | | | - Noboru Imai
- Department of Neurology, Japanese Red Cross Shizuoka Hospital, Shizuoka 420-0853, Japan
| | - Junichi Miyahara
- Headache Center and Department of Neurology, Tominaga Hospital, Osaka 556-0017, Japan
| | | | - Yoshihiko Nakazato
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Kazuki Fujita
- Department of Neurology, Jichi Medical University Saitama Medical Center, Saitama 330-8503, Japan
| | - Eri Hoshino
- Department of Neurology, Saitama Neuropsychiatric Institute, Saitama 338-8577, Japan
| | - Toshimasa Yamamoto
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
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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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Friedman DI. Approach to the Patient With Headache. Continuum (Minneap Minn) 2024; 30:296-324. [PMID: 38568485 DOI: 10.1212/con.0000000000001413] [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: 04/05/2024]
Abstract
OBJECTIVE The evaluation of patients with headache relies heavily on the history. This article reviews key questions for diagnosing primary and secondary headache disorders with a rationale for each and phrasing to optimize the information obtained and the patient's experience. LATEST DEVELOPMENTS The availability of online resources for clinicians and patients continues to increase, including sites that use artificial intelligence to generate a diagnosis and report based on patient responses online. Patient-friendly headache apps include calendars that help track treatment response, identify triggers, and provide educational information. ESSENTIAL POINTS A structured approach to taking the history, incorporating online resources and other technologies when needed, facilitates making an accurate diagnosis and often eliminates the need for unnecessary testing. A detailed yet empathetic approach incorporating interpersonal skills enhances relationship building and trust, both of which are integral to successful treatment.
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Khan L, Shahreen M, Qazi A, Jamil Ahmed Shah S, Hussain S, Chang HT. Migraine headache (MH) classification using machine learning methods with data augmentation. Sci Rep 2024; 14:5180. [PMID: 38431729 PMCID: PMC10908834 DOI: 10.1038/s41598-024-55874-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.
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Affiliation(s)
- Lal Khan
- Department of Computer Science, Ibadat International University Islamabad Pakpattan Campus, Pakpattan, Pakistan
| | - Moudasra Shahreen
- Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Pakistan
| | - Atika Qazi
- Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam
| | | | - Sabir Hussain
- Department of Agriculture, Mir Chakar Khan Rind University, Sibi, Pakistan
| | - Hsien-Tsung Chang
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
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Torrente A, Maccora S, Prinzi F, Alonge P, Pilati L, Lupica A, Di Stefano V, Camarda C, Vitabile S, Brighina F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci 2024; 14:85. [PMID: 38248300 PMCID: PMC10813497 DOI: 10.3390/brainsci14010085] [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: 12/22/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.
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Affiliation(s)
- Angelo Torrente
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Simona Maccora
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology Unit, ARNAS Civico di Cristina and Benfratelli Hospitals, 90127 Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Paolo Alonge
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Laura Pilati
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology and Stroke Unit, P.O. “S. Antonio Abate”, 91016 Trapani, Italy
| | - Antonino Lupica
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Vincenzo Di Stefano
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Cecilia Camarda
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Filippo Brighina
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
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7
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Wei HL, Wei C, Feng Y, Yan W, Yu YS, Chen YC, Yin X, Li J, Zhang H. Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images. iScience 2023; 26:108107. [PMID: 37867961 PMCID: PMC10585394 DOI: 10.1016/j.isci.2023.108107] [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: 05/11/2023] [Revised: 07/19/2023] [Accepted: 09/27/2023] [Indexed: 10/24/2023] Open
Abstract
Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Cunsheng Wei
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Yibo Feng
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Jiangsu Province, Nanjing 210006, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Jiangsu Province, Nanjing 210006, China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu 211100, China
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Young NP, Ridgeway JL, Haddad TC, Harper SB, Philpot LM, Christopherson LA, McColley SM, Phillips SA, Brown JK, Zimmerman KS, Ebbert JO. Feasibility and Usability of a Mobile App-Based Interactive Care Plan for Migraine in a Community Neurology Practice: Development and Pilot Implementation Study. JMIR Form Res 2023; 7:e48372. [PMID: 37796560 PMCID: PMC10587810 DOI: 10.2196/48372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Migraine is a common and major cause of disability, poor quality of life, and high health care use. Access to evidence-based migraine care is limited and projected to worsen. Novel mobile health app-based tools may effectively deliver migraine patient education to support self-management, facilitate remote monitoring and treatment, and improve access to care. The risk that such an intervention may increase the care team workload is a potential implementation barrier. OBJECTIVE This study aims to describe a novel electronic health record-integrated mobile app-based Migraine Interactive Care Plan (MICP) and evaluate its feasibility, usability, and impact on care teams in a community neurology practice. METHODS Consecutive enrollees between September 1, 2020, and February 16, 2022, were assessed in a single-arm observational study of usability, defined by 74.3% (127/171) completing ≥1 assigned task. Task response rates, rate and type of care team escalations, and patient-reported outcomes were summarized. Patients were prospectively recruited and randomly assigned to routine care with or without the MICP from September 1, 2020, to September 1, 2021. Feasibility was defined by equal to or fewer downstream face-to-face visits, telephone contacts, and electronic messages in the MICP cohort. The Wilcoxon rank-sum test was used to compare continuous variables, and the chi-square test was used for categorical variables for those with at least 3 months of follow-up. RESULTS A total of 171 patients were enrolled, and of these, 127 (74.3%) patients completed ≥1 MICP-assigned task. Mean escalations per patient per month was 0.9 (SD 0.37; range 0-1.7). Patient-confirmed understanding of the educational materials ranged from 26.6% (45/169) to 56.2% (95/169). Initial mean headache days per week was 4.54 (SD 2.06) days and declined to 2.86 (SD 1.87) days at week 26. The percentage of patients reporting favorable satisfaction increased from a baseline of 35% (20/57) to 83% (15/18; response rate of 42/136, 30.9% to 28/68, 41%) over the first 6 months. A total of 121 patients with MICP were compared with 62 patients in the control group. No differences were observed in the rate of telephone contacts or electronic messages. Fewer face-to-face visits were observed in the MICP cohort (13/121, 10.7%) compared with controls (26/62, 42%; P<.001). CONCLUSIONS We describe the successful implementation of an electronic health record-integrated mobile app-based care plan for migraine in a community neurology practice. We observed fewer downstream face-to-face visits without increasing telephone calls, medication refills, or electronic messages. Our findings suggest that the MICP has the potential to improve patient access without increasing care team workload and the need for patient input from diverse populations to improve and sustain patient engagement. Additional studies are needed to assess its impact in primary care.
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Affiliation(s)
- Nathan P Young
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Integrated Community Specialty Practice, Mayo Clinic, Rochester, MN, United States
| | - Jennifer L Ridgeway
- Division of Health Care Delivery Research, Mayo Clinic, Rochester, MN, United States
| | - Tufia C Haddad
- Department of Oncology, Mayo Clinic, Rochester, MN, United States
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Sarah B Harper
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Lindsey M Philpot
- Community Internal Medicine, Mayo Clinic, Rochester, MN, United States
- Qualitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | | | - Samantha M McColley
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
- Clinical Informatics and Practice Support, Mayo Clinic, Rochester, MN, United States
| | - Sarah A Phillips
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Julie K Brown
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Kelly S Zimmerman
- Integrated Community Specialty Practice, Mayo Clinic, Rochester, MN, United States
| | - Jon O Ebbert
- Community Internal Medicine, Mayo Clinic, Rochester, MN, United States
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9
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Katsuki M, Matsumori Y, Kawamura S, Kashiwagi K, Koh A, Tachikawa S, Yamagishi F. Developing an artificial intelligence-based diagnostic model of headaches from a dataset of clinic patients' records. Headache 2023; 63:1097-1108. [PMID: 37596885 DOI: 10.1111/head.14611] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/15/2023] [Accepted: 06/28/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVE We developed an artificial intelligence (AI)-based headache diagnosis model using a large questionnaire database from a headache-specializing clinic. BACKGROUND Misdiagnosis of headache disorders is a serious issue and AI-based headache diagnosis models are scarce. METHODS We developed an AI-based headache diagnosis model and conducted internal validation based on a retrospective investigation of 6058 patients (4240 training dataset for model development and 1818 test dataset for internal validation) diagnosed by a headache specialist. The ground truth was the diagnosis by the headache specialist. The diagnostic performance of the AI model was evaluated. RESULTS The dataset included 4829/6058 (79.7%) patients with migraine, 834/6058 (13.8%) with tension-type headache, 78/6058 (1.3%) with trigeminal autonomic cephalalgias, 38/6058 (0.6%) with other primary headache disorders, and 279/6058 (4.6%) with other headaches. The mean (standard deviation) age was 34.7 (14.5) years, and 3986/6058 (65.8%) were female. The model's micro-average accuracy, sensitivity (recall), specificity, precision, and F-values for the test dataset were 93.7%, 84.2%, 84.2%, 96.1%, and 84.2%, respectively. The diagnostic performance for migraine was high, with a sensitivity of 88.8% and c-statistics of 0.89 (95% confidence interval 0.87-0.91). CONCLUSIONS Our AI model demonstrated high diagnostic performance for migraine. If secondary headaches can be ruled out, the model can be a powerful tool for diagnosing migraine; however, further data collection and external validation are required to strengthen the performance, ensure the generalizability in other outpatients, and demonstrate its utility in real-world settings.
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Affiliation(s)
- Masahito Katsuki
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | | | - Shin Kawamura
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Kenta Kashiwagi
- Department of Neurology, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Akihito Koh
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Senju Tachikawa
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Fuminori Yamagishi
- Department of Surgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
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10
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Zhang P. Which headache disorders can be diagnosed concurrently? An analysis of ICHD3 criteria using prime encoding system. Front Neurol 2023; 14:1221209. [PMID: 37670775 PMCID: PMC10475541 DOI: 10.3389/fneur.2023.1221209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
Introduction Real-life headache presentations may fit more than one ICHD3 diagnosis. This project seeks to exhaustively list all logically consistent "co-diagnoses" according to the ICHD3 criteria. We limited our project to cases of two concurrent diagnoses. Methods We included the criteria for "Migraine" (1.1, 1.2, 1.3), "Tension-type headache" (2.1, 2.2, 2.3, 2.4), "Trigeminal autonomic cephalalgias" (3.1, 3.2, 3.3, 3.4, 3.5), and "Other primary headache disorders." We also excluded "probable" diagnosis criteria. Each characteristic in the above criteria is assigned a unique prime number. We then encoded each ICHD3 criteria into integers through multiplication in a list format; we called these criteria representations. "Codiagnoses representations" were generated by multiplying all possible pairings of criteria representations. We then manually encoded a list of logically inconsistent characteristics through multiplication. All co-diagnoses representations divisible by any inconsistency representations were filtered out, generating a list of co-diagnoses representations that were logically consistent. This list was then translated back into ICHD3 diagnoses. Results We used a total of 103 prime numbers to encode 578 ICHD3 criteria. Once illogical characteristics were excluded, we obtained 145 dual diagnoses. Of the dual diagnoses, two contained intersecting characteristics due to subset relationships, 14 contained intersecting characteristics without subset relationships, and 129 contained dual diagnoses as a result of non-intersecting characteristics. Conclusion Analysis of dual diagnosis in headaches offers insight into "loopholes" in the ICHD3 as well as a potential explanation for the source of a number of controversies regarding headache disorders. The existence of dual diagnoses and their identification may carry implications for future developments and testing of machine-learning diagnostic algorithms for headaches.
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Affiliation(s)
- Pengfei Zhang
- Department of Neurology, Rutgers Robert University Medical School, New Brunswick, NJ, United States
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11
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Gazerani P. Intelligent Digital Twins for Personalized Migraine Care. J Pers Med 2023; 13:1255. [PMID: 37623505 PMCID: PMC10455577 DOI: 10.3390/jpm13081255] [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: 07/21/2023] [Revised: 08/04/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023] Open
Abstract
Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.
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Affiliation(s)
- Parisa Gazerani
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway;
- Centre for Intelligent Musculoskeletal Health (CIM), Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Gistrup, Denmark
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12
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Sasaki S, Katsuki M, Kawahara J, Yamagishi C, Koh A, Kawamura S, Kashiwagi K, Ikeda T, Goto T, Kaneko K, Wada N, Yamagishi F. Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model. Cureus 2023; 15:e44415. [PMID: 37791157 PMCID: PMC10543415 DOI: 10.7759/cureus.44415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Misdiagnosis of pediatric and adolescent migraine is a significant problem. The first artificial intelligence (AI)-based pediatric migraine diagnosis model was made utilizing a database of questionnaires obtained from a previous epidemiological study, the Itoigawa Benizuwaigani Study. Methods The AI-based headache diagnosis model was created based on the internal validation based on a retrospective investigation of 909 patients (636 training dataset for model development and 273 test dataset for internal validation) aged six to 17 years diagnosed based on the International Classification of Headache Disorders 3rd edition. The diagnostic performance of the AI model was evaluated. Results The dataset included 234/909 (25.7%) pediatric or adolescent patients with migraine. The mean age was 11.3 (standard deviation 3.17) years. The model's accuracy, sensitivity (recall), specificity, precision, and F-values for the test dataset were 94.5%, 88.7%, 96.5%, 90.0%, and 89.4%, respectively. Conclusions The AI model exhibited high diagnostic performance for pediatric and adolescent migraine. It holds great potential as a powerful tool for diagnosing these conditions, especially when secondary headaches are ruled out. Nonetheless, further data collection and external validation are necessary to enhance the model's performance and ensure its applicability in real-world settings.
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Affiliation(s)
- Shiori Sasaki
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Masahito Katsuki
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Junko Kawahara
- Department of Health Promotion, Itoigawa City, Itoigawa, JPN
| | | | - Akihito Koh
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Shin Kawamura
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Kenta Kashiwagi
- Department of Neurology, Itoigawa General Hospital, Itoigawa, JPN
| | - Takashi Ikeda
- Department of Health Promotion, Itoigawa City, Itoigawa, JPN
| | - Tetsuya Goto
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Kazuma Kaneko
- Department of Neurology, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Naomichi Wada
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
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13
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Teshima THN, Zakrzewska JM, Potter R. A systematic review of screening diagnostic tools for trigeminal neuralgia. Br J Pain 2023; 17:255-266. [PMID: 37342400 PMCID: PMC10278451 DOI: 10.1177/20494637221146854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Background and objective Trigeminal neuralgia (TN) is a rare chronic neuropathic pain condition of sudden and severe pain, often described as an electric shock. Diagnosis is challenging for non-expert clinicians, particularly in primary care settings. We wanted to identify and assess the diagnostic accuracy of existing screening tools for TN and orofacial pain that could be used to support the diagnosis of TN in primary care. Databases and data treatment We searched key databases (MEDLINE, ASSIA, Embase, and Web of Knowledge and PsycINFO) supplemented by citation tracking from January 1988 to 2021. We used an adapted version of the Quality of Diagnostic Accuracy Studies (QUADAS-2) to assess the methodological quality of each study. Results Searches identified five studies, from the UK, USA and Canada; three validated self-report questionnaires; and two artificial neural networks. All screened for multiple orofacial pain diagnoses, including dentoalveolar pain, musculoskeletal pain (temporomandibular disorders) and neurological pain (trigeminal neuralgia, headache, atypical facial pain and postherpetic neuralgia). The overall quality assessment was low for one study. Conclusions Diagnosis of TN can be challenging for non-expert clinicians. Our review found few existing screening tools to diagnose TN, and none is currently suitable to be used in primary care settings. This evidence supports the need to adapt an existing tools or to create a new tool for this purpose. The development of an appropriate screening questionnaire could assist non-expert dental and medical clinicians to identify TN more effectively and empower them to manage or refer patients for treatment more effectively.
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Affiliation(s)
- THN Teshima
- Department of Oral Medicine, UCL Eastman Dental Institute, London, UK
- Department of Oral Medicine and Pain Education Research Centre at ULCH NHS Foundation Trust, Royal National ENT & Eastman Dental Hospitals, ULCH NHS Foundation Trust, London, UK
| | - JM Zakrzewska
- Department of Oral Medicine, UCL Eastman Dental Institute, London, UK
- Department of Oral Medicine and Pain Education Research Centre at ULCH NHS Foundation Trust, Royal National ENT & Eastman Dental Hospitals, ULCH NHS Foundation Trust, London, UK
| | - R Potter
- Warwick Clinical Trials Unit, Coventry, UK
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14
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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15
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Lu ZX, Dong BQ, Wei HL, Chen L. Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment. Front Pharmacol 2022; 13:1002080. [PMID: 36532762 PMCID: PMC9754055 DOI: 10.3389/fphar.2022.1002080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 11/10/2022] [Indexed: 12/10/2023] Open
Abstract
Background: The selection strategy of non-steroidal anti-inflammatory drugs (NSAIDs) for migraine is hard to judge whether it is effective, leading to unnecessary exposure to insufficient or lengthy treatment trials. The goal of the study was to investigate potential predictors of NSAIDs efficacy in migraine therapy and to explore their influence on efficacy. Methods: 610 migraine patients were recruited and assigned into responders and non-responders. Potential predictors among demographic and clinical characteristics for NSAIDs efficacy were extracted using multivariable logistic regression (LR) analysis, and were applied to construct prediction models via machine learning (ML) algorithms. Finally, Cochran-Mantel-Haenszel tests were used to examine the impact of each predictor on drug efficacy. Results: Multivariate LR analysis revealed migraine-related (disease duration, headache intensity and frequency) and psychiatric (anxiety, depression and sleep disorder) characteristics were predictive of NSAIDs efficacy. The accuracies of ML models using support vector machine, decision tree and multilayer perceptron were 0.712, 0.741, and 0.715, respectively. Cochran-Mantel-Haenszel test showed that, for variables with homogeneity of odds ratio, disease duration, frequency, anxiety, and depression and sleep disorder were associated with decreased likelihood of response to all NSAIDs. However, the variabilities in the efficacy of acetaminophen and celecoxib between patients with mild and severe headache intensity were not confirmed. Conclusion: Migraine-related and psychiatric parameters play a critical role in predicting the outcomes of acute migraine treatment. These models based on predictors could optimize drug selection and improve benefits from the start of treatment.
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Affiliation(s)
- Zhao-Xuan Lu
- Department of Interventional and Vascular Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Bing-Qing Dong
- Department of Radiology, Kunshan Hospital of Traditional Chinese Medicine, Kunshan, China
| | - Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu, China
| | - Liang Chen
- Department of Interventional and Vascular Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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16
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Vandenbussche N, Van Hee C, Hoste V, Paemeleire K. Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache. J Headache Pain 2022; 23:129. [PMID: 36180844 PMCID: PMC9524092 DOI: 10.1186/s10194-022-01490-0] [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: 06/30/2022] [Accepted: 09/01/2022] [Indexed: 11/18/2022] Open
Abstract
Background Headache medicine is largely based on detailed history taking by physicians analysing patients’ descriptions of headache. Natural language processing (NLP) structures and processes linguistic data into quantifiable units. In this study, we apply these digital techniques on self-reported narratives by patients with headache disorders to research the potential of analysing and automatically classifying human-generated text and information extraction in clinical contexts. Methods A prospective cross-sectional clinical trial collected self-reported narratives on headache disorders from participants with either migraine or cluster headache. NLP was applied for the analysis of lexical, semantic and thematic properties of the texts. Machine learning (ML) algorithms were applied to classify the descriptions of headache attacks from individual participants into their correct group (migraine versus cluster headache). Results One-hundred and twenty-one patients (81 participants with migraine and 40 participants with cluster headache) provided a self-reported narrative on their headache disorder. Lexical analysis of this text corpus resulted in several specific key words per diagnostic group (cluster headache: Dutch (nl): “oog” | English (en): “eye”, nl: “pijn” | en: “pain” and nl: “terug” | en: “back/to come back”; migraine: nl: “hoofdpijn” | en: “headache”, nl: “stress” | en: “stress” and nl: “misselijkheid” | en: “nausea”). Thematic and sentiment analysis of text revealed largely negative sentiment in texts by both patients with migraine and cluster headache. Logistic regression and support vector machine algorithms with different feature groups performed best for the classification of attack descriptions (with F1-scores for detecting cluster headache varying between 0.82 and 0.86) compared to naïve Bayes classifiers. Conclusions Differences in lexical choices between patients with migraine and cluster headache are detected with NLP and are congruent with domain expert knowledge of the disorders. Our research shows that ML algorithms have potential to classify patients’ self-reported narratives of migraine or cluster headache with good performance. NLP shows its capability to discern relevant linguistic aspects in narratives from patients with different headache disorders and demonstrates relevance in clinical information extraction. The potential benefits on the classification performance of larger datasets and neural NLP methods can be investigated in the future. Trial registration This study was registered with clinicaltrials.gov with ID NCT05377437. Supplementary Information The online version contains supplementary material available at 10.1186/s10194-022-01490-0.
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Affiliation(s)
- Nicolas Vandenbussche
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium. .,Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium.
| | - Cynthia Van Hee
- LT3 - Language and Translation Technology Team, Department of Translation, Interpreting and Communication, Faculty of Arts and Philosophy, Ghent University, Groot-Brittanniëlaan 45, B-9000, Ghent, Belgium
| | - Véronique Hoste
- LT3 - Language and Translation Technology Team, Department of Translation, Interpreting and Communication, Faculty of Arts and Philosophy, Ghent University, Groot-Brittanniëlaan 45, B-9000, Ghent, Belgium
| | - Koen Paemeleire
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium.,Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium
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17
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Daripa B, Lucchese S. Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review. Cureus 2022; 14:e29514. [PMID: 36299975 PMCID: PMC9588408 DOI: 10.7759/cureus.29514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2022] [Indexed: 11/30/2022] Open
Abstract
Wielding modern technology in the form of artificial intelligence (AI) or deep learning (DL) can utilize the best possible latest computer application in intricate decision-making and enigmatic problem-solving. It has been recommended in many fields. However, it is a long way from achieving an ambitious genuine intention when it comes to understanding and identifying any headache condition or classification, and using it error-free. No studies hitherto formalized any headache AI models to accurately classify headaches. A machine’s job can be arduous when incorporating an emotional dimension in decision making, re-challenging its own diagnosis by keeping a differential at all times, where even experienced neurologists or headache experts sometimes find it demanding to make a precise analysis and formulate a methodical plan. This could be because of spanning clinical presentation at a given moment of time or a change in clinical pattern over time which apparently could be due to intercrossing multiple pathophysiologies. We did a short literature review on the role of artificial intelligence and machine learning in headache classification. This brings forth a minuscule insight into the vastness of headaches and the perpetual effort and exploration headache may demand from AI when trying to scrutinize its classification. Undoubtedly, AI or DL could better be utilized in identifying the red flags of headache, as it might help our patients at home or the primary care physicians/practicing doctors/non- neurologists in their clinic to triage the headache patients if they need an imperative higher center referral to a neurologist for advanced evaluation. This outlook can limit the burden on a handful of headache specialists by minimizing the referrals to a tertiary care setting.
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18
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Wei HL, Yang WJ, Zhou GP, Chen YC, Yu YS, Yin X, Li J, Zhang H. Altered static functional network connectivity predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without aura. Front Mol Neurosci 2022; 15:956797. [PMID: 36176962 PMCID: PMC9513180 DOI: 10.3389/fnmol.2022.956797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/22/2022] [Indexed: 11/25/2022] Open
Abstract
Brain networks have significant implications for the understanding of migraine pathophysiology and prognosis. This study aimed to investigate whether large-scale network dysfunction in patients with migraine without aura (MwoA) could predict the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs). Seventy patients with episodic MwoA and 33 healthy controls (HCs) were recruited. Patients were divided into MwoA with effective NSAIDs (M-eNSAIDs) and with ineffective NSAIDs (M-ieNSAIDs). Group-level independent component analysis and functional network connectivity (FNC) analysis were used to extract intrinsic networks and detect dysfunction among these networks. The clinical characteristics and FNC abnormalities were considered as features, and a support vector machine (SVM) model with fivefold cross-validation was applied to distinguish the subjects at an individual level. Dysfunctional connections within seven networks were observed, including default mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), dorsal attention network (DAN), visual network (VN), and auditory network (AN). Compared with M-ieNSAIDs and HCs, patients with M-eNSAIDs displayed reduced DMN-VN and SMN-VN, and enhanced VN-AN connections. Moreover, patients with M-eNSAIDs showed increased FNC patterns within ECN, DAN, and SN, relative to HCs. Higher ECN-SN connections than HCs were revealed in patients with M-ieNSAIDs. The SVM model demonstrated that the area under the curve, sensitivity, and specificity were 0.93, 0.88, and 0.89, respectively. The widespread FNC impairment existing in the modulation of medical treatment suggested FNC disruption as a biomarker for advancing the understanding of neurophysiological mechanisms and improving the decision-making of therapeutic strategy.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, Nanjing Jiangning Hospital, Nanjing, China
| | - Wen-Juan Yang
- Department of Neurology, Nanjing Jiangning Hospital, Nanjing, China
| | - Gang-Ping Zhou
- Department of Radiology, Nanjing Jiangning Hospital, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Sheng Yu
- Department of Radiology, Nanjing Jiangning Hospital, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junrong Li
- Department of Neurology, Nanjing Jiangning Hospital, Nanjing, China
| | - Hong Zhang
- Department of Radiology, Nanjing Jiangning Hospital, Nanjing, China
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Woldeamanuel YW, Cowan RP. Computerized migraine diagnostic tools: a systematic review. Ther Adv Chronic Dis 2022; 13:20406223211065235. [PMID: 35096362 PMCID: PMC8793115 DOI: 10.1177/20406223211065235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/18/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Computerized migraine diagnostic tools have been developed and validated since 1960. We conducted a systematic review to summarize and critically appraise the quality of all published studies involving computerized migraine diagnostic tools. METHODS We performed a systematic literature search using PubMed, Web of Science, Scopus, snowballing, and citation searching. Cutoff date for search was 1 June 2021. Published articles in English that evaluated a computerized/automated migraine diagnostic tool were included. The following summarized each study: publication year, digital tool name, development basis, sample size, sensitivity, specificity, reference diagnosis, strength, and limitations. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was applied to evaluate the quality of included studies in terms of risk of bias and concern of applicability. RESULTS A total of 41 studies (median sample size: 288 participants, median age = 43 years; 77% women) were included. Most (60%) tools were developed based on International Classification of Headache Disorders criteria, half were self-administered, and 82% were evaluated using face-to-face interviews as reference diagnosis. Some of the automated algorithms and machine learning programs involved case-based reasoning, deep learning, classifier ensemble, ant-colony, artificial immune, random forest, white and black box combinations, and hybrid fuzzy expert systems. The median diagnostic accuracy was concordance = 89% [interquartile range (IQR) = 76-93%; range = 45-100%], sensitivity = 87% (IQR = 80-95%; range = 14-100%), and specificity = 90% (IQR = 77-96%; range = 65-100%). Lack of random patient sampling was observed in 95% of studies. Case-control designs were avoided in all studies. Most (76%) reference tests exhibited low risk of bias and low concern of applicability. Patient flow and timing showed low risk of bias in 83%. CONCLUSION Different computerized and automated migraine diagnostic tools are available with varying accuracies. Random patient sampling, head-to-head comparison among tools, and generalizability to other headache diagnoses may improve their utility.
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Affiliation(s)
- Yohannes W. Woldeamanuel
- Division of Headache & Facial Pain, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Robert P. Cowan
- Division of Headache & Facial Pain, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
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Sudershan A, Mahajan K, Singh K, Dhar MK, Kumar P. The Complexities of Migraine: A Debate Among Migraine Researchers: A Review. Clin Neurol Neurosurg 2022; 214:107136. [DOI: 10.1016/j.clineuro.2022.107136] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/29/2021] [Accepted: 01/16/2022] [Indexed: 12/21/2022]
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21
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Paulina Vistoso Monreal A, Veas N, Clark G. An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain. JAPANESE DENTAL SCIENCE REVIEW 2021; 57:242-249. [PMID: 34849180 PMCID: PMC8608603 DOI: 10.1016/j.jdsr.2021.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 11/26/2022] Open
Abstract
This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being explored are based predominately on clinical history (subjective) and physical examination (objective) information. The system has been piloted and refined using patient encounters collected in a clinic specializing in Orofacial Pain treatment. In summary, whether you believe a branching hybrid check-box based data collection system with built-in algorithms is needed, depends on your individual agenda. If you have no plans for data analysis or publishing about the various phenotypes discovered and you do not need pop-up suggestions for best diagnosis and treatment options, it is easier to use a semi-structured narrative note for your patient encounters. If, however, you want data-driven diagnostic and disease risk algorithms and pop-up best-treatment options, then you need a highly structured data collection system that is compatible with machine learning analysis. Automating the journey from data collection to diagnoses has the potential to improve standards of care by providing faster and reliable predictions.
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Affiliation(s)
| | - Nicolas Veas
- McCombs School of Business, The University of Texas, Austin, TX, USA
| | - Glenn Clark
- Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, USA
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22
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Katsuki M, Narita N, Matsumori Y, Ishida N, Watanabe O, Cai S, Tominaga T. Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surg Neurol Int 2020; 11:475. [PMID: 33500813 PMCID: PMC7827501 DOI: 10.25259/sni_827_2020] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 12/10/2020] [Indexed: 12/12/2022] Open
Abstract
Background Primary headaches are widespread and costly public health problems. However, there are insufficient medical resources for their treatment in Japan due to two reasons. First, the numbers of headache specialists and clinics remain insufficient. Second, neurologists and neurosurgeons mainly treat headaches in Japan. However, they mainly work as general stroke neurologists, so they cannot focus on primary headache treatment. To solve these problems, we preliminarily developed a deep learning (DL)-based automated diagnosis model from patients' Japanese unstructured sentences in the medical questionnaire using a DL framework. We hypothesized that the model would reduce the time and burden on both doctors and patients and improve their quality of life. Methods We retrospectively investigated our primary headache database and developed a diagnosis model using the DL framework (Prediction One, Sony Network Communications Inc., Japan). We used age, sex, date, and embedding layer made by the medical questionnaire's natural language processing (NLP). Results Eight hundred and forty-eight primary headache patients (495 women and 353 men) are included. The median (interquartile range) age was 59 (40-74). Migraine accounted for 46%, tension-type headache for 47%, trigeminal autonomic cephalalgias for 5%, and other primary headache disorders for 2%. The accuracy, mean precision, mean recall, and mean F value of the developed diagnosis model were 0.7759, 0.8537, 0.6086, and 0.6353, which were satisfactory. Conclusion The DL-based diagnosis model for primary headaches using the raw medical questionnaire's Japanese NLP would be useful in performing efficient medical practice after ruling out the secondary headaches.
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Affiliation(s)
- Masahito Katsuki
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi, Japan
| | - Norio Narita
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi, Japan
| | | | - Naoya Ishida
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi, Japan
| | - Ohmi Watanabe
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi, Japan
| | - Siqi Cai
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Aobaku, Sendai, Miyagi, Japan
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