From Radiologist Report to Image Label: Assessing Latent Dirichlet Allocation in Training Neural Networks for Orthopedic Radiograph Classification
This work addresses improving radiograph interpretation for orthopedic clinics, but it is incremental as it applies existing NLP and neural network methods to a specific medical dataset.
The study tackled the problem of classifying orthopedic trauma radiographs by using Latent Dirichlet Allocation (LDA) to generate image labels from radiologist reports and training a neural network, achieving accuracies ranging from 60% to 91% depending on the label. They found LDA was unsuited for high-accuracy labeling, but the neural network could still detect some features effectively.
Background: Radiography (X-rays) is the dominant modality in orthopedics, and improving the interpretation of radiographs is clinically relevant. Machine learning (ML) has revolutionized data analysis and has been applied to medicine, with some success, in the form of natural language processing (NLP) and artificial neural networks (ANN). Latent Dirichlet allocation (LDA) is an NLP method that automatically categorizes documents into topics. Successfully applying ML to orthopedic radiography could enable the creation of computer-aided decision systems for use in the clinic. We studied how an automated ML pipeline could classify orthopedic trauma radiographs from radiologist reports. Methods: Wrist and ankle radiographs from Danderyd Hospital in Sweden taken between 2002 and 2015, with radiologist reports. LDA was used to create image labels for radiographs from the radiologist reports. Radiographs and labels were used to train an image recognition ANN. The ANN outcomes were manually reviewed to get an accurate estimate of the method's utility and accuracy. Results: Image Labels generated via LDA could successfully train the ANN. The ANN reached an accuracy between 91% and 60% compared to a gold standard, depending on the label. Conclusions: We found that LDA was unsuited to label orthopedic radiographs from reports with high accuracy. However, despite this, the ANN could learn to detect some features in radiographs with high accuracy. The study also illustrates how ML and ANN can be applied to medical research.