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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.


Introduction And Background
Headache is mostly an intricate multifarious brain manifestation to manage, despite how simple it looks. Even experienced neurologists or headache experts sometimes find it demanding to make a precise diagnosis and formulate a methodical plan. This could be because of a spanning clinical presentation apparently due to intercrossing multiple pathophysiologies. Wielding modern technology in 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, but then it is a long way from achieving an ambitious genuine intention when comes to understanding and identifying any headache condition or classification, and using it error-free [1,2].
Successful utilization of AI is appreciated in law and regulation, plant disease, and medical problems [1]. For instance, its utility in hypertension diagnosis, drug discovery, and nephropathy detection among newborns is well known [3]. Furthermore, its application in cancer-associated thrombosis risk assessment and breast cancer progression risk calculation is noteworthy [4]. There are many more applications in the process of development like Alzheimer's and Parkinson's disease diagnosis and brain tumor classification [5].
A primary headache is a subjective phenomenon. It is challenging to integrate technology asking to make it a diagnosis considering that we do not have any objective parameters to feed into or utilize in a computerized expert system as done in the above medical situations for effortless interpretation. Constant endeavors from engineering and technology may bring high-yielding outcomes; thus, AI should be coaxed for the betterment of mankind and the healthcare system. Headache has many undeciphered dimensions. Hence, the current era demands an AI technology that can auto-upgrade and sculpt as per the new updates in this discipline.

Method
We did a literature search in Pub med and Google scholar. Title searches were "artificial intelligence (AI) in headache classification", "machine learning (ML) in headache classification" or "deep learning use in headache classification". After excluding the duplicates, only 12 studies were included that were written in the English language and met the above search criteria. Studies were from technology and technical science, neuroscience, engineering, medicine, and emergency medicine department, from 2010 to December 2021. Studies included projects, where a set of questionnaires were used, at least classified one headache type by using assorted algorithms, or where set-up models were employed in the emergency departments to codify headaches. Opinions about AI on headaches or unpublished projects were also included. As our understanding of computer programming, software, and detailed working pattern of these sophisticated models/technology was limited, we did not elaborate on the algorithmic logic, processing, and application part.

Result
The inference drawn from various studies from the department of technology, engineering and neurosciences conducted worldwide expressing the application of AI in headache classification are charted, summarized and illustrated well in a tabular format below (

Discussion
Identification of focal cortical dysplasia, the evolvement of neuroimaging biomarkers in Alzheimer's and prognosticate clinical consequences in depression therapeutics undoubtedly prove AI's success and expanding boundaries [7]. With regards to migraine and cluster headaches, there can be a functional variation in terms of activation of separate structures in the brain, namely the trigeminovascular system, brainstem, hypothalamus and cortical areas. There could be an adjoining structural alteration of cortical thickness and its surface area. Perhaps, these changes could be related to ictal or interictal phases, and may be dynamic in nature [7]. The use of ML algorithms in functional and morphometric MRI analysis helps to distinguish these headache features [7]. It is appropriate to mention here that AI has invested a decade's drudgery in recognizing just one headache disorder: migraine [4].
In short, numerous AI models, namely artificial neural networks (ANNs), artificial immune system (AIS) or support vector machines (SVM) are grappling to diagnose/categorize the phenotypes of only one headache type as they need further studies and validation; the exemplary model hunt is on-going. A handful of studies from the past provide little insight into AI's influence on headache as illustrated above in table 1. and the perpetual effort that headache demands from AI when attempting to explore its own classification can be well anticipated from their limitations. Machine learning or deep learning has already been used in scouting neuroimages related to headaches [2]. In the time ahead, we anticipate AI/ML may provide neuroimage brain 'signatures' for specific headache categories, aided by clinical data [4].

Challenges AI May Face Over The Classification of Headaches And Our Vewpoint
The application of patient-oriented digital health gadgets can shrink the healthcare-pertinent levy as evidenced by telehealth utilization in the current COVID-19 pandemic. E-diary based headache monitoring, ML-driven products predicting headache onset or digital wearables identifying sleep time or even smartphone aided biofeed-back are a few cardinal achievements for our patients [14].
Adroit clinical interviews and reminiscence is the archetype when diagnosing primary headache which undoubtedly necessitates a holistic approach and involves neurophysiological, neuroimaging or blood biomarkers to which AI's headache database, library and engineering part relies upon [2]. Overlapping clinical presentation at a given moment of time, change in clinical pattern over time and keeping a differential diagnosis at all times are challenges that may be confronted by AI. Imparting an emotionally delicate touch from the patient's side of conversation and being empathetic from the physician's end is difficult to simulate by AI technology.
It is interesting to note that one study attempted to classify emergency headache types using AI (particularly primary as well as secondary headache types). Undoubtedly, it excellently explored the secondary headache category with high sensitivity, specificity and negative predictive value while using an AI model, but meagerly addressed the primary emergent headache [6]. Optimum utilization and guidance of AI technology is not only limited to diagnosing probable migraine or probable TTH to help our general practitioner [12], but could also be extended to identify any alarming headache condition.
In our view, AI or DL could 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 urgent higher center referral to a neurologist for advance evaluation, or whether they can continue with the current treatment in their clinic or home. This would minimize patient's as well as doctor's judgment errors, lessen panic situations, and finally both would feel confident when participating in a mutual discussion about symptom management. This approach can limit the burden on a handful of headache specialists by minimizing the referrals to a tertiary care setting.

Conclusions
Supervised ML algorithms use training data which are processed and model trained to predict or classify a test data/input into predefined groups with the help of a 'classifier' whereas unsupervised machine learning models process input/test data via algorithm and vectors of a training set to declare result in a cluster format. Subtle changes in the brain can be picked up with AI neuroimaging for a better understanding of the disease state as mentioned earlier. However, AI/DL needs to combine anamnesis, clinical signs, and dynamic functional variation with adjoining structural imaging alteration to accurately recognize headache types. Ironically, no studies hitherto formalized any headache AI models to accurately classify headaches.
AI needs a multidisciplinary approach from neurology, neurosurgery, neuroradiology, technology, engineering, and opinions from various other related disciplines for accelerated work that should undoubtedly be patient-centric. In the words of Gandhi, 'The patient is the most important person in our hospital. He is not dependent on us. We are dependent on him. We are not doing him a favor by serving him.
He is doing us a favor by giving us an opportunity to do so.'

Conflicts of interest:
In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.