Computerized migraine diagnostic tools: a systematic review

Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management

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.

Artificial intelligence and headache

BACKGROUND AND METHODS

In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives.

RESULTS

We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management.

CONCLUSIONS

The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.

Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study

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.

Approach to the Patient With Headache

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.

Developing an artificial intelligence-based diagnostic model of headaches from a dataset of clinic patients' records

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.

Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model

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.

The First Case Series From Japan of Primary Headache Patients Treated by Completely Online Telemedicine

Background Since March 2020, the coronavirus disease 2019 pandemic has increased the need for telemedicine to avoid in-person consultations. Online clinics for most diseases officially started in Japan in April 2022. Here, we report the cases of eight Japanese headache patients treated by completely online telemedicine for three months from the first visit. Methodology From the medical records between July 2022 and October 2022, we retrospectively investigated eight consecutive first-visit primary headache patients who consulted our online headache clinic via telemedicine and continued to see us via telemedicine only. The Headache Impact Test-6 (HIT-6) score, monthly headache days (MHD), and monthly acute medication intake days (AMD) were investigated over the observation period. Results A total of eight women were included, and the median (interquartile range) age was 30 (24-51) years. The median HIT-6 scores before, one, and three months after treatment were 63 (58-64), 54 (53-62), and 52 (49-54), respectively. MHD before, one, and three months after treatment were 15 (9-28), 12 (3-17), and 2 (2-8), respectively. AMD before, one, and three months after treatment were 10 (3-13), 3 (1-8), and 2 (0-3), respectively. Significant reductions in HIT-6 and MDH were observed three months after the initial consultation (p = 0.007 and p = 0.042, respectively). AMD was not significantly decreased at three months (p = 0.447). Conclusions This is the first report of Japanese patients treated by completely online telemedicine for three months from the first visit. HIT-6 and MDH can be significantly decreased at three months by only telemedicine. Online telemedicine is expected to be widely used to resolve unmet needs in headache treatment.

Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review

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.

Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study

Objective

This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria.

Background

Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis.

Methods

Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.

Results

Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively.

Conclusion

The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.

Artificial Intelligence and Migraine: Insights and Applications

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