Cytomegalovirus infection causes diffuse reduction in ADC values in the fetal brain even in unremarkable fetal MR imaging scans. Cytomegalovirus-infected children with unremarkable fetal MR imaging scans do not deviate from the healthy population in the VABS-II neurocognitive assessment. ADC values were not correlated with VABS-II scores. However, the lack of clinical findings, as seen in most cytomegalovirus-infected fetuses, does not eliminate the possibility of future neurodevelopmental pathology.

Objectives: To assess the effect of a commercial Artificial Intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single Level 1 Trauma Center. Methods: A retrospective cohort study for two time periods – Pre-AI (1.1.2017-1.1.2018) and Post-AI (1.1.2019-1.1.2020), in a Level 1 Trauma Center was performed. Participants older than 18 years with a confirmed diagnosis of ICH on head CT upon admission to the emergency department were collected. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke –IS; and myocardial infarction - MI) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. Secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. Results: 587 participants (289 Pre-AI – age 71 ± 1, 169 men; 298 Post-AI – age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality weresignificantly reduced in the Post-AI group when compared to the Pre-AI group (27.7% vs 17.5%; p=0.004 and 31.8% vs 21.7%; p=0.017 respectively).Modified Rankin Scale (mRS) at discharge was significantly reduced Post-AI implementation (3.2 vs 2.8; p=0.044). Conclusion:Implementation of an AI based computer aided triage and prioritization solution for flagging participants with ICH in an emergent care setting coincided with significant reductions of 30- and 120-day all-cause mortality and morbidity.

Background To assess the effect of a commercial artificial intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single level 1 trauma center. Methods A retrospective cohort study for two time periods—pre-AI (1.1.2017–1.1.2018) and post-AI (1.1.2019–1.1.2020)—in a level 1 trauma center was performed. The ICH algorithm was applied to 587 consecutive patients with a confirmed diagnosis of ICH on head CT upon admission to the emergency department. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke (IS) and myocardial infarction (MI)) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. The secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. Results Five hundred eighty-seven participants (289 pre-AI—age 71 ± 1, 169 men; 298 post-AI—age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH, and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality were significantly reduced in the post-AI group when compared to the pre-AI group (27.7% vs 17.5%; p = 0.004 and 31.8% vs 21.7%; p = 0.017, respectively). Modified Rankin Scale (mRS) at discharge was significantly reduced post-AI implementation (3.2 vs 2.8; p = 0.044). Conclusion The added value of this study emphasizes the introduction of artificial intelligence (AI) computer-aided triage and prioritization software in an emergent care setting that demonstrated a significant reduction in a 30- and 120-day all-cause mortality and morbidity for patients diagnosed with intracranial hemorrhage (ICH). Along with mortality rates, the AI software was associated with a significant reduction in the Modified Ranking Scale (mRs).