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.

In-Hospital Amantadine Does Not Improve Outcomes After Severe Traumatic Brain Injury: An 11-Year Propensity-Matched Retrospective Analysis.

BACKGROUND: Disruption of dopamine neurotransmission is associated with functional impairment after severe traumatic brain injury (sTBI). This has prompted the study of dopamine agonists, such as amantadine, to assist recovery of consciousness. Randomized trials have mostly addressed the posthospital setting, with inconsistent findings. Therefore, we evaluated the efficacy of early amantadine administration on recovery of consciousness after sTBI. METHODS: We searched the medical records of all patients with sTBI admitted to our hospital between 2010 and 2021 who survived 10 days postinjury. We identified all patients receiving amantadine and compared them with all patients not receiving amantadine and a propensity score-matched nonamantadine group. Primary outcome measures included discharge Glasgow Coma Scale, Glasgow Outcome Scale-Extended score, length of stay, mortality, recovery of command-following (CF), and days to CF. RESULTS: In our study population, 60 patients received amantadine and 344 did not. Compared with the propensity score-matched nonamantadine group, the amantadine group had no difference in mortality (86.67% vs. 88.33%, P = 0.783), rates of CF (73.33% vs. 76.67%, P = 0.673), or percentage of patients with severe (3-8) discharge Glasgow Coma Scale scores (11.11% vs. 12.28%, P = 0.434). In addition, the amantadine group was less likely to have a favorable recovery (discharge Glasgow Outcome Scale-Extended score 5-8) (14.53% vs. 16.67%, P < 0.001), had a longer length of stay (40.5 vs. 21.0 days, P < 0.001), and had a longer time to CF (11.5 vs. 6.0 days, P = 0.011). No difference in adverse events existed between groups. CONCLUSIONS: Our findings do not support the early administration of amantadine for sTBI. Larger inpatient randomized trials are necessary to further investigate amantadine treatment for sTBI.

Predicting traumatic brain injury outcomes using a posterior dominant rhythm.

OBJECTIVE: Predicting severe traumatic brain injury (sTBI) outcomes is challenging, and existing models have limited applicability to individual patients. This study aimed to identify metrics that could predict recovery following sTBI. The researchers strived to demonstrate that a posterior dominant rhythm on electroencephalography is strongly associated with positive outcomes and to develop a novel machine learning-based model that accurately forecasts the return of consciousness. METHODS: In this retrospective study, the authors assessed all intubated adults admitted with sTBI (Glasgow Coma Scale [GCS] score ≤ 8) from 2010 to 2021, who underwent EEG recording < 30 days from sTBI (n = 195). Seventy-three clinical, radiographic, and EEG variables were collected. Based on the presence of a PDR within 30 days of injury, two cohorts were created-those with a PDR (PDR[+] cohort, n = 51) and those without (PDR[-] cohort, n = 144)-to assess differences in presentation and four outcomes: in-hospital survival, recovery of command following, Glasgow Outcome Scale-Extended (GOS-E) score at discharge, and GOS-E score at 6 months post discharge. AutoScore, a machine learning-based clinical score generator that selects and assigns weights to important predictive variables, was used to create a prognostic model that predicts in-hospital survival and recovery of command following. Lastly, the MRC-CRASH and IMPACT traumatic brain injury predictive models were used to compare expected patient outcomes with true outcomes. RESULTS: At presentation, the PDR(-) cohort had a lower mean GCS motor subscore (1.97 vs 2.45, p = 0.048). Despite no difference in predicted outcomes (via MRC-CRASH and IMPACT), the PDR(+) cohort had superior rates of in-hospital survival (84.3% vs 63.9%, p = 0.007), recovery of command following (76.5% vs 53.5%, p = 0.004), and mean discharge GOS-E score (3.00 vs 2.39, p = 0.006). There was no difference in the 6-month GOS-E score. AutoScore was then used to identify the 7 following variables that were highly predictive of in-hospital survival and recovery of command: age, body mass index, systolic blood pressure, pupil reactivity, blood glucose, and hemoglobin (all at presentation), and a PDR on EEG. This model had excellent discrimination for predicting in-hospital survival (area under the curve [AUC] 0.815) and recovery of command following (AUC 0.700). CONCLUSIONS: A PDR on EEG in sTBI patients predicts favorable outcomes. The authors' prognostic model has strong accuracy in predicting these outcomes, and performed better than previously reported models. The authors' model can be valuable in clinical decision-making as well as counseling families following these types of injuries.

Prioritization of social information by the basolateral amygdala in rats.

The amygdala is a collection of nuclei that support adaptive social behavior and are implicated in disorders such as autism. The basolateral complex of the amygdala (BLA), a main subdivision of the amygdala, influences fear responses, motivated behavior, and memory of emotional events via its communication with other amygdalar nuclei and with other brain regions such as the prefrontal cortex, striatum, and hippocampus. The specific role of the BLA in responses to social stimuli is less clear. The present study of female rats investigated the role of the BLA in responding to socially-relevant information by asking how inactivation of the BLA with bilateral infusions of the GABA receptor agonist muscimol would affect spontaneous exploration of wood blocks scented either with conspecific male or female urine or with nonsocial odorants. Conspecific urine samples were used because urine conveys information about sex, health, social status, and reproductive state in rodents. The results revealed that BLA inactivation reduced female rats' spontaneous preference for social odors over nonsocial odors, specifically for female urine. However, BLA inactivation did not generally impair rats' ability to distinguish two odors from the same category (e.g., urine odors from two different male rats). The results indicate that the BLA is important for responding to salience of social stimuli but not for discriminating between different individuals, a result that has important implications for amygdalar modulation of downstream attention, motivation, and memory processes for social stimuli.