Machine learning analysis of confounding variables of a convolutional neural network specific for abdominal aortic aneurysms.

Objective: To identify confounding variables influencing the accuracy of a convolutional neural network (CNN) specific for infrarenal abdominal aortic aneurysms (AAAs) on computed tomography angiograms (CTAs). Methods: A Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, retrospective study analyzed abdominopelvic CTA scans from 200 patients with infrarenal AAAs and 200 propensity-matched control patients. An AAA-specific trained CNN was developed by the application of transfer learning to the VGG-16 base model using model training, validation, and testing techniques. Model accuracy and area under the curve were analyzed based on data sets (selected, balanced, or unbalanced), aneurysm size, extra-abdominal extension, dissections, and mural thrombus. Misjudgments were analyzed by review of heatmaps, via gradient weighted class activation, overlaid on CTA images. Results: The trained custom CNN model reported high test group accuracies of 94.1%, 99.1%, and 99.6% and area under the curve of 0.9900, 0.9998, and 0.9993 in selected (n = 120), balanced (n = 3704), and unbalanced image sets (n = 31,899), respectively. Despite an eightfold difference between balanced and unbalanced image sets, the CNN model demonstrated high test group sensitivities (98.7% vs 98.9%) and specificities (99.7% vs 99.3%) in unbalanced and balanced image sets, respectively. For aneurysm size, the CNN model demonstrates decreasing misjudgments as aneurysm size increases: 47% (16/34) for aneurysms <3.3 cm, 32% (11/34) for aneurysms 3.3 to 5 cm, and 20% (7/34) for aneurysms >5 cm. Aneurysms containing measurable mural thrombus were over-represented within type II (false-negative) misjudgments compared with type I (false-positive) misjudgments (71% vs 15%, P < .05). Inclusion of extra-abdominal aneurysm extension (thoracic or iliac artery) or dissection flaps in these imaging sets did not decrease the model's overall accuracy, indicating that the model performance was excellent without the need to clean the data set of confounding or comorbid diagnoses. Conclusions: Analysis of an AAA-specific CNN model can accurately screen and identify infrarenal AAAs on CTA despite varying pathology and quantitative data sets. The highest anatomic misjudgments were with small aneurysms (<3.3 cm) or the presence of mural thrombus. Accuracy of the CNN model is maintained despite the inclusion of extra-abdominal pathology and imbalanced data sets.

Development of a convolutional neural network to detect abdominal aortic aneurysms.

Objective: We sought to train a foundational convolutional neural network (CNN) for screening computed tomography (CT) angiography (CTA) scans for the presence of infrarenal abdominal aortic aneurysms (AAAs) for future predictive modeling and other artificial intelligence applications. Methods: From January 2015 to January 2020, a HIPAA (Health Insurance and Accountability Act)-compliant, institutional review board-approved, retrospective clinical study analyzed contrast-enhanced abdominopelvic CTA scans from 200 patients with infrarenal AAAs and 200 propensity-matched control patients with non-aneurysmal infrarenal abdominal aortas. A CNN was trained to binary classification on the input. For model improvement and testing, transfer learning using the ImageNet database was applied to the VGG-16 base model. The image dataset was randomized to sets of 60%, 10%, and 30% for model training, validation, and testing, respectively. A stochastic gradient descent was used for optimization. The models were assessed by testing validation accuracy and the area under the receiver operating characteristic curve. Results: Preliminary data demonstrated a nonrandom pattern of accuracy and detectability. Iterations (≤10) of the model characteristics generated a final custom CNN model reporting an accuracy of 99.1% and area under the receiver operating characteristic curve of 0.99. Misjudgments were analyzed through review of the heat maps generated via gradient weighted class activation mapping overlaid on the original CT images. The greatest misjudgments were seen in small aneurysms (<3.3 cm) with mural thrombus. Conclusions: Preliminary data from a CNN model have shown that the model can accurately screen and identify CTA findings of infrarenal AAAs. This model serves as a proof-of-concept to proceed with potential future directions to include expansion to predictive modeling and other artificial intelligence-based applications.

Acute Hyperglycemia Does Not Affect Brain Swelling or Infarction Volume After Middle Cerebral Artery Occlusion in Rats.

Stroke disproportionally affects diabetic and hyperglycemic patients with increased incidence and is associated with higher morbidity and mortality due to brain swelling. In this study, the intraluminal suture middle cerebral artery occlusion (MCAO) model was used to examine the effects of blood glucose on brain swelling and infarct volume in acutely hyperglycemic rats and normo-glycemic controls. Fifty-four rats were distributed into normo-glycemic sham surgery, hyperglycemic sham surgery, normo-glycemic MCAO, and hyperglycemic MCAO. To induce hyperglycemia, 15 min before MCAO surgery, animals were injected with 50 % dextrose. Animals were subjected to 90 min of MCAO and sacrificed 24 h after reperfusion for hemispheric brain swelling and infarct volume calculations using standard equations. While normo-glycemic and hyperglycemic animals after MCAO presented with significantly higher brain swelling and larger infarcts than their respective controls, no statistical difference was observed for either brain swelling or infarct volume between normo-glycemic shams and hyperglycemic shams or normo-glycemic MCAO animals and hyperglycemic MCAO animals. The findings of this study suggest that blood glucose does not have any significant effect on hemispheric brain swelling or infarct volume after MCAO in rats.

Functional radiography in examination of spondylolisthesis.

OBJECTIVE: Despite the predominant use of standing flexion-extension radiography for quantifying instability in isthmic and degenerative spondylolisthesis, other functional radio-graphic techniques have been presented in the literature. CONCLUSION: The current evidence reported in the literature is insufficient to influence how the results of these other functional radiographic techniques should affect clinical management; however, it does raise doubts regarding the accuracy and reliability of standing flexion-extension radiography in this setting. Based on the currently available evidence and until randomized studies are performed to assess the efficacy of functional radiographic techniques in directing clinical decision making, positioning schemes other than traditional standing flexion-extension may be considered as options in the evaluation of patients with symptomatic isthmic and degenerative spondylolisthesis in which standard flexion-extension radiographs fail to show pathologic instability.