Use of Artificial Intelligence in Clinical Neurology.

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.

Teaching the Neurological Examination in a Rapidly Evolving Clinical Climate.

The neurological examination remains the essence of neurology. It allows symptoms to be assessed, diagnoses to be made, and dynamic functions to be followed. Skill in the neurological examination has faced increasing challenges from the encroachment of diagnostic imaging, but has maintained its clinical utility. It has also encountered the battle for the precious time within a medical curriculum. This review considers how the neurological examination can best be taught into the future. It does so by considering factors related to the examination, the learner, the teacher, and the modern clinical environment.

Neurology Training Worldwide.

Neurology training is essential for providing neurologic care globally. Large disparities in availability of neurology training exist between higher- and lower-income countries. This review explores the worldwide distribution of neurology training programs and trainees, the characteristics of training programs in different parts of the world, and initiatives aimed at increasing access to neurology training in under-resourced regions.

Cuprizone demyelination induces a unique inflammatory response in the subventricular zone.

BACKGROUND: Cuprizone leads to demyelination of the corpus callosum (CC) and activates progenitor cells in the adjacent subventricular zone (SVZ), a stem cell niche which contributes to remyelination. The healthy SVZ contains semi-activated microglia and constitutively expresses the pro-inflammatory molecule galectin-3 (Gal-3) suggesting the niche uniquely regulates inflammation. METHODS: We studied the inflammatory response to cuprizone in the SVZ and CC in Gal-3 knockout mice using immunohistochemistry and with the in vitro neurosphere assay. RESULTS: Cuprizone caused loss of myelin basic protein (MBP) immunofluorescence in the CC suggesting demyelination. Cuprizone increased the density of CD45+/Iba1+ microglial cells and also increased Gal-3 expression in the CC. Surprisingly, the number of Gal-3+ and CD45+ cells decreased in the SVZ after cuprizone, suggesting inflammation was selectively reduced therein. Inflammation can regulate SVZ proliferation and indeed the number of phosphohistone H3+ (PHi3+) cells decreased in the SVZ but increased in the CC in both genotypes after cuprizone treatment. BrdU+ SVZ cell numbers also decreased in the SVZ after cuprizone, and this effect was significantly greater at 3 weeks in Gal-3 (-/-) mice compared to WT, suggesting Gal-3 normally limits SVZ cell emigration following cuprizone treatment. CONCLUSIONS: This study reveals a uniquely regulated inflammatory response in the SVZ and shows that Gal-3 participates in remyelination in the cuprizone model. This contrasts with more severe models of demyelination which induce SVZ inflammation and suggests the extent of demyelination affects the SVZ neurogenic response.

Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head.

BACKGROUND AND PURPOSE: Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) with consensus neuroradiologists' interpretations in detecting mass effect and vasogenic edema. MATERIALS AND METHODS: A retrospective stand-alone performance assessment was conducted on data sets of noncontrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients 18 years of age or older from 5 hospitals in the United States. The positive cases were selected consecutively on the basis of the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up-to-three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the artificial intelligence model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The artificial intelligence model separately received thin (≤1.5 mm) and/or thick (>1.5 and ≤5 mm) axial series. RESULTS: The 2 cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The artificial intelligence model identified mass effect with a sensitivity of 96.6% (95% CI, 94.9%-98.2%) and a specificity of 89.8% (95% CI, 84.7%-94.2%) for the thin series, and 95.3% (95% CI, 93.5%-96.8%) and 93.1% (95% CI, 89.1%-96.6%) for the thick series. It identified vasogenic edema with a sensitivity of 90.2% (95% CI, 82.0%-96.7%) and a specificity of 93.5% (95% CI, 88.9%-97.2%) for the thin series, and 90.0% (95% CI, 84.0%-96.0%) and 95.5% (95% CI, 92.5%-98.0%) for the thick series. The corresponding areas under the curve were at least 0.980. CONCLUSIONS: The assessed artificial intelligence model accurately identified mass effect and vasogenic edema in this CT data set. It could assist the clinical workflow by prioritizing interpretation of cases with abnormal findings, possibly benefiting patients through earlier identification and subsequent treatment.

The potential clinical utility of an artificial intelligence model for identification of vertebral compression fractures in chest radiographs.

PURPOSE: To assess the ability of the Annalise Enterprise CXR Triage Trauma artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment. MATERIALS AND METHODS: This retrospective study used a consecutive cohort of 596 chest radiographs from four U.S. hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related ICD-10 diagnostic codes and medication use for the study period and an additional year of follow up. RESULTS: The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% CI: 0.939 to 0.968), sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity 89.2% (95% CI: 85.4 to 92.3%). Out of the 236 true-positive cases (i.e., correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease modifying medication for osteoporosis. CONCLUSION: The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity of 89.2% (95% CI: 85.4 to 92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease modifying medications.

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm.

Purpose: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation. Methods: With IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 and March 2022 for the following terms: "motion artifacts", "respiratory motion", "technically inadequate", and "suboptimal" or "limited exam". All CTPA reports were from two quaternary (Site A, n = 335; B, n = 259) and a community (C, n = 199) healthcare sites. A thoracic radiologist reviewed CT images of all positive hits for motion artifacts (present or absent) and their severity (no diagnostic effect or major diagnostic impairment). Coronal multiplanar images from 793 CTPA exams were de-identified and exported offline into an AI model building prototype (Cognex Vision Pro, Cognex Corporation) to train an AI model to perform two-class classification ("motion" or "no motion") with data from the three sites (70% training dataset, n = 554; 30% validation dataset, n = 239). Separately, data from Site A and Site C were used for training and validating; testing was performed on the Site B CTPA exams. A five-fold repeated cross-validation was performed to evaluate the model performance with accuracy and receiver operating characteristics analysis (ROC). Results: Among the CTPA images from 793 patients (mean age 63 ± 17 years; 391 males, 402 females), 372 had no motion artifacts, and 421 had substantial motion artifacts. The statistics for the average performance of the AI model after five-fold repeated cross-validation for the two-class classification included 94% sensitivity, 91% specificity, 93% accuracy, and 0.93 area under the ROC curve (AUC: 95% CI 0.89-0.97). Conclusion: The AI model used in this study can successfully identify CTPA exams with diagnostic interpretation limiting motion artifacts in multicenter training and test datasets. Clinical relevance: The AI model used in the study can help alert technologists about the presence of substantial motion artifacts on CTPA, where a repeat image acquisition can help salvage diagnostic information.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Head CT deep learning model is highly accurate for early infarct estimation.

Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.

Importance: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. Objective: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. Design, Setting, and Participants: This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. Main Outcomes and Measures: The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points were the sensitivities and specificities for the detection of pneumothorax and tension pneumothorax. Results: The final analysis included radiographs from 985 patients (mean [SD] age, 60.8 [19.0] years; 436 [44.3%] female patients), including 307 patients with nontension pneumothorax, 128 patients with tension pneumothorax, and 550 patients without pneumothorax. The AI model detected any pneumothorax with an AUC of 0.979 (95% CI, 0.970-0.987), sensitivity of 94.3% (95% CI, 92.0%-96.3%), and specificity of 92.0% (95% CI, 89.6%-94.2%) and tension pneumothorax with an AUC of 0.987 (95% CI, 0.980-0.992), sensitivity of 94.5% (95% CI, 90.6%-97.7%), and specificity of 95.3% (95% CI, 93.9%-96.6%). Conclusions and Relevance: These findings suggest that the assessed AI model accurately detected pneumothorax and tension pneumothorax in this chest radiograph data set. The model's use in the clinical workflow could lead to earlier identification and improved care for patients with pneumothorax.