Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.

Introduction: Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods: We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results: The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion: The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

A quantitative model of the cerebral windkessel and its relevance to disorders of intracranial dynamics.

OBJECTIVE: Traditional models of intracranial dynamics fail to capture several important features of the intracranial pressure (ICP) pulse. Experiments show that, at a local amplitude minimum, the ICP pulse normally precedes the arterial blood pressure (ABP) pulse, and the cranium is a band-stop filter centered at the heart rate for the ICP pulse with respect to the ABP pulse, which is the cerebral windkessel mechanism. These observations are inconsistent with existing pressure-volume models. METHODS: To explore these issues, the authors modeled the ABP and ICP pulses by using a simple electrical tank circuit, and they compared the dynamics of the circuit to physiological data from dogs by using autoregressive with exogenous inputs (ARX) modeling. RESULTS: The authors' ARX analysis showed close agreement between the circuit and pulse suppression in the canine cranium, and they used the analogy between the circuit and the cranium to examine the dynamics that underlie this pulse suppression. CONCLUSIONS: This correspondence between physiological data and circuit dynamics suggests that the cerebral windkessel consists of the rhythmic motion of the brain parenchyma and CSF that continuously opposes systolic and diastolic blood flow. Such motion has been documented with flow-sensitive MRI. In thermodynamic terms, the direct current (DC) power of cerebral arterial perfusion drives smooth capillary flow and alternating current (AC) power shunts pulsatile energy through the CSF to the veins. This suggests that hydrocephalus and related disorders are disorders of CSF path impedance. Obstructive hydrocephalus is the consequence of high CSF path impedance due to high resistance. Normal pressure hydrocephalus (NPH) is the consequence of high CSF path impedance due to low inertance and high compliance. Low-pressure hydrocephalus is the consequence of high CSF path impedance due to high resistance and high compliance. Ventriculomegaly is an adaptive physiological response that increases CSF path volume and thereby reduces CSF path resistance and impedance. Pseudotumor cerebri is the consequence of high DC power with normal CSF path impedance. CSF diversion by shunting is an accessory windkessel-it drains energy (and thereby lowers ICP) and lowers CSF path resistance and impedance. Cushing's reflex is an accessory windkessel in extremis-it maintains DC power (arterial hypertension) and reduces AC power (bradycardia). The windkessel theory is a thermodynamic approach to the study of energy flow through the cranium, and it points to a new understanding of hydrocephalus and related disorders.

Agitation Following Severe Traumatic Brain Injury Is a Clinical Sign of Recovery of Consciousness.

Objective: Severe traumatic brain injury (sTBI) often results in disorders of consciousness. Patients emerging from coma frequently exhibit aberrant behaviors such as agitation. These non-purposeful combative behaviors can interfere with medical care. Interestingly, agitation is associated with arousal and is often among the first signs of neurological recovery. A better understanding of these behaviors may shed light on the mechanisms driving the return of consciousness in sTBI patients. This study aims to investigate the association between posttraumatic agitation and the recovery of consciousness. Methods: A retrospective chart review was conducted in 530 adult patients (29.1% female) admitted to Stony Brook University Hospital between January 2011 and December 2019 with a diagnosis of sTBI and Glasgow Coma Scale (GCS) ≤8. Agitation was defined as a Richmond Agitation Sedation Scale (RASS) > +1, or any documentation of equivalently combative and violent behaviors in daily clinical notes. The ability to follow verbal commands was used to define the recovery of consciousness and was assessed daily. Results: Of 530 total sTBI patients, 308 (58.1%) survived. Agitation was present in 169 of all patients and 162 (52.6%) of surviving patients. A total of 273 patients followed commands, and 159 of them developed agitation. Forty patients developed agitation on hospital arrival whereas 119 developed agitation later during their hospital course. Presence of in-hospital agitation positively correlated with command-following (r = 0.315, p < 0.001). The time to develop agitation and time to follow commands showed positive correlation (r = 0.485, p < 0.001). These two events occurred within 3 days in 54 (44.6%) patients, within 7 days in 81 (67.8%) patients, and within 14 days in 96 (80.2%) patients. In 71 (59.7%) patients, agitation developed before command-following; in 36 (30.2%) patients, agitation developed after command-following; in 12 (10.1%) patients, agitation developed on the same day as command-following. Conclusion: Posttraumatic agitation in comatose patients following sTBI is temporally associated with the recovery of consciousness. This behavior indicates the potential for recovery of higher neurological functioning. Further studies are required to identify neural correlates of posttraumatic agitation and recovery of consciousness after sTBI.