Automated Femoral Neck Shaft Angle Measurement Using Neural Networks

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1 SIIM 2017 Scientific Session Posters & Demonstrations Automated Femoral Neck Shaft Angle Using s Simcha Rimler, MD, SUNY Downstate; Srinivas Kolla; Mohammad Faysel; Menachem Rimler (Presenter); Gabriel Karkenny; Benjamin Parnes Hypothesis Introduction Methods A trained recurrent neural network will demonstrate visual recognition capability by measuring the femoral neck-shaft angle with a median absolute difference of less than 15 degrees compared to a radiologist. Non-recurrent neural networks are a subgroup of neural networks in which the potential synapses between neurons are restricted by the requirement that a neuron s output cannot affect itself, either directly or indirectly through intermediate neurons. Recurrent neural networks remove this restriction. Although recurrent neural networks are harder to train, they do provide increased flexibility and increased biological plausibility. This abstract proposes that a recurrent neural network can overcome some of the limitations of modern visual recognition software. We demonstrate this by training a recurrent neural network to correctly measure the femoral neck-shaft angle. This is the first report in the literature of achieving this task. The specific aim of the project was to use supervised learning to train a recurrent neural network on a set of x-rays of adult normal right hips whose femoral neck-shaft angles were measured manually. This neural network would then be tested with a second group of x-rays of adult normal right hips whose femoral neck-shaft angle was unknown. The network measurements of the test cases would be compared to the measurements of a radiologist who was blind to the results of the neural network. The goal was to achieve a median absolute difference of less than 15 degrees compared to the radiologist. This means that half the time the measurement of the neural network was off by 15 degrees or less. Figure 1

2 A spiking recurrent network was coded in C++. The network consisted of 32,768 neurons. Information entered the network through 7,720 input neurons (analogous in function to axons in the optic nerve) and exited through 20 output neurons. Anonymized images of right hip x-rays were retrieved from PACS and assessed by the researchers for quality. 80 studies were selected to form the training and validation sets and the femoral neck -shaft angle was measured by the researchers. An additional 40 studies comprised the testing set. The training set together with the researcher s measurements were fed into the neural network. The simulation used an event driven model following Watts. The simulation was performed on a single core of an Intel Xeon CPU 2.80GHz with 12 GiB. The trained network was tested with the 40 images from the testing set. A radiologist who was board certified in musculoskeletal imaging and who was blinded to the results of the neural network provided measurements for these test images. The results of the neural network and the radiologist were compared using wxmaxima. Figure 2 Results The simulation ran for 32 hours 47 minutes during which 19 hours 39 minutes of neural network activity was simulated. The median absolute difference between the neural network and the radiologist was 4 degrees which is more accurate than our goal of 15 degrees. Our mean square error was Graph 1 Graph 1. Histogram of Value of s

3 The height of each bar represents the number of studies whose discrepancy between the neural network and the radiologist was within the given range. Table 1 Most Accurate Table 2 Median Table 3 Least Accurate Tables 1-3. Representative Sections of the s of the and the The results of the training cases are ordered by accuracy. Subsections of the full data set are shown to demonstrate the most accurate examples, the examples with median accuracy, and the least accurate examples. (The full data set is available upon request.)

4 Conclusions We present a preliminary report of a recurrent neural network s ability to analyze an image and provide potentially useful information. We measured the success of the network by the median absolute difference of its output when compared to a radiologist s measurement of the femoral neck-shaft angles on unknown X-rays. We defined a successfully trained network as one that achieves a median absolute difference of less than 15 degrees. The neural network achieved a median absolute difference of 4 degrees which means that half the time its output was within 4 degrees of the radiologist's measurement. Our model has more than surpassed our criteria for demonstrating visual recognition capability and providing useful information. This study reinforces theoretical arguments for the potential of recurrent neural networks to overcome the limitations previously encountered in non-recurrent networks, and visual recognition software in general. A more refined version of this neural network can be used in many healthcare related settings to increase the accuracy and efficiency of radiographic image interpretations. Limitations and Areas of Further Research References To properly support our theory that recurrent synapses are improving the program s visual recognition capabilities, another study is needed with a comparable non-recurrent neural network as a control. One intrinsic complication of our analysis was the use of a radiologist as the gold standard for femoral neck shaft angle measurement. More accurate techniques have been described in the literature including the use of cadavers (Kay) and CT (Weiner). Theoretically, a more accurate gold standard can produce a more a more accurate network. Additionally, we performed this study on a standard desktop to demonstrate the potential versatility of recurrent neural networks, however a larger recurrent neural network needs to be run on a more powerful device to sufficiently demonstrate the potentials of this design strategy. In our experimental design, we chose median absolute error as our measurement of proficiency with a cutoff of 15 degrees as an indication of success. Achieving and surpassing these criteria is no indication that the criteria we chose actually reflects proficiency. Therefore, we also reported our mean square error which is This compares favorably to inter-radiologist measurements of femoral neck shaft angles in the literature. More development and study is needed to improve the architecture and enhance the learning capabilities of these networks, specifically regarding synaptic plasticity. Furthermore, a methodology needs to be developed to analyze a network so that we can investigate not just the output of the neural network, but also the logical pathways it utilizes to arrive at that conclusion. 1. Bland JM and Altman DG. (2003) Applying the Right Statistics: Analyses of Studies. Ultrasound in Obstetrics and Gynecology, 22, Doherty M, Courtney P, Doherty S, Jenkins W, Maciewicz RA, Muir K, Zhang W. Nonspherical femoral head shape (pistol grip deformity), neck shaft angle, and risk of hip osteoarthritis: a case-control study. Arthritis Rheum Oct;58(10): Kay, Robert M., Kai A. Jaki, and David L. Skaggs. "The effect of femoral rotation on the projected femoral neck-shaft angle." Journal of Pediatric Orthopaedics 20.6 (2000): Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. "On the difficulty of training recurrent neural networks." ICML (3) 28 (2013):

5 5. Watts, Lloyd. "Event-driven simulation of networks of spiking neurons." Advances in neural information processing systems (1994): Weiner, Dennis S., et al. "Computed tomography in the measurement of femoral anteversion." Orthopedics 1.4 (1978): Xu, Xu, Hao Ge, and Shenghong Li. "An improvement on recurrent neural network by combining convolution neural network and a simple initialization of the weights." Online Analysis and Computing Science (ICOACS), IEEE International Conference of. IEEE, Keywords machine learning, neural networks, artificial intelligence, CAD, recurrent neural network