Daripa Bob, Lucchese Scott
Internal Medicine/Neurology, Singapore General Hospital, Singapore, SGP.
General Physician, Grant Government Medical College and Sir J.J. Group of Hospitals, Mumbai, IND.
Cureus. 2022 Sep 23;14(9):e29514. doi: 10.7759/cureus.29514. eCollection 2022 Sep.
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.
运用人工智能(AI)或深度学习(DL)形式的现代技术,可以在复杂的决策和难以解决的问题中运用尽可能最新的最佳计算机应用程序。这在许多领域都得到了推荐。然而,在理解和识别任何头痛病症或分类并无误地使用它方面,距离实现雄心勃勃的真正意图还有很长的路要走。迄今为止,尚无研究将任何头痛AI模型正式化以准确分类头痛。当在决策中纳入情感维度,始终保持鉴别诊断从而再次挑战自身诊断时,机器的工作可能很艰巨,即使是经验丰富的神经科医生或头痛专家有时也会觉得进行精确分析并制定有条理的计划很困难。这可能是因为在特定时刻临床表现的跨度,或者随着时间推移临床模式的变化,这显然可能是由于多种病理生理学相互交叉所致。我们对人工智能和机器学习在头痛分类中的作用进行了简短的文献综述。这对头痛的广泛性以及在试图仔细审查其分类时AI可能需要对头痛付出的持续努力和探索提供了微不足道的见解。毫无疑问,AI或DL可以更好地用于识别头痛的警示信号,因为它可能有助于在家中的患者或基层医疗医生/执业医生/非神经科医生在诊所对头痛患者进行分诊,如果他们需要紧急转诊到更高一级的中心由神经科医生进行进一步评估。这种前景可以通过减少向三级医疗机构的转诊来减轻少数头痛专家的负担。