Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables (1811.03695v1)

Abstract: Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs. Computer-Aided Diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep learning models on 17,587 radiographs to classify fracture, five patient traits, and 14 hospital process variables. All 20 variables could be predicted from a radiograph (p < 0.05), with the best performances on scanner model (AUC=1.00), scanner brand (AUC=0.98), and whether the order was marked "priority" (AUC=0.79). Fracture was predicted moderately well from the image (AUC=0.78) and better when combining image features with patient data (AUC=0.86, p=2e-9) or patient data plus hospital process features (AUC=0.91, p=1e-21). The model performance on a test set with matched patient variables was significantly lower than a random test set (AUC=0.67, p=0.003); and when the test set was matched on patient and image acquisition variables, the model performed randomly (AUC=0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's predictive ability overall. We also used Naive Bayes to combine evidence from image models with patient and hospital data and found their inclusion improved performance, but that this approach was nevertheless inferior to directly modeling all variables. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep learning decision processes so that computers and clinicians can effectively cooperate.

• The paper demonstrates that integrating radiographs with patient and hospital process data boosts hip fracture prediction from an AUC of 0.78 to 0.91.
• The paper employs an Inception-v3 CNN and ensemble frameworks to extract features and control confounding variables via matched cohort analysis.
• The paper highlights the importance of multimodal inputs and confounding control to develop reliable and generalizable diagnostic deep learning systems.

Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables

The paper titled "Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables" evaluates the application of deep learning (DL) models in predicting hip fractures from radiographic images, considering a wide array of both patient-specific and healthcare process-related variables. Hip fractures are significant predictors of morbidity and mortality among the elderly. While automated detection through DL presents an opportunity to aid radiologists in diagnosing these fractures, there exists a challenge in ensuring that these models are generalizable and not biased by irrelevant confounding variables.

Key Findings

The authors trained convolutional neural networks (CNNs) on 17,587 radiographs, leveraging an extensive dataset comprising fracture data, patient demographics, and healthcare process variables such as scanner model and order priority. The results demonstrated the model's capability to predict all 20 variables from radiographs, with notable performance in determining the scanner model (AUC=1.00) and brand (AUC=0.98), as well as the priority of the order (AUC=0.79). When it came to predicting fractures, the standalone image model achieved an AUC of 0.78, which improved to 0.86 when combined with patient data and further to 0.91 with the addition of hospital process features.

Methodology and Analytical Insights

This paper used the Inception-v3 model architecture, originally fine-tuned with ImageNet data, to derive image features for each radiograph. Logistic regression and Naive Bayes models were deployed in ensemble frameworks to integrate these image-derived features with patient and hospital metadata. The paper noteworthy employed a novel approach, where it assessed covariate impact through matched cohort analysis to disentangle the model's reliance on potentially confounding variables, revealing that model performance degraded when these were controlled.

Implications

The paper opens avenues for integrating diverse datasets into DL models, emphasizing the necessity of multi-modal inputs for reliable medical diagnoses. It highlights the prevailing risk of models inadvertently learning from biased datasets and the importance of accounting for such biases to improve resilience across varied clinical scenarios.

Future Directions

While the paper establishes a framework for multimodal integration and robust evaluation, further efforts are needed to enhance transparency and understanding of DL decision-making processes. Potential areas for exploration include adversarial network training and domain adaptation strategies to minimize reliance on non-biological signals. Furthermore, the inclusion of higher-resolution datasets and more comprehensive annotations could markedly enhance model precision and applicability. Future research might also focus on real-world trials examining the synergistic usage of these models in a clinical decision-support role, potentially leading toward more autonomous, yet safely calibrated systems for medical diagnostics.

In conclusion, this paper presents a comprehensive approach to deploying DL in fracture detection, underlining both the promise and challenges associated with incorporating confounding patient and healthcare variables. As healthcare AI advances, such rigorous examinations are critical to ensuring these technologies' effective and ethical deployment in clinical practice.