Using Artificial Intelligence Algorithms to Detect Vertebral Fractures

Using Artificial Intelligence Algorithms to Detect Vertebral Fractures

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Using Artificial Intelligence Algorithms to Detect Vertebral Fractures
Artificial intelligence can augment existing diagnostic capabilities for spine specialists. Learn how a Japanese team tested AI diagnosis against experienced spine surgeons, and hear from an expert on the tech’s potential.
The Future
Use of image processing technology employing artificial intelligence (AI) has increased in recent years across an array of medical imaging disciplines. Particularly, deep learning technologies are receiving much of the attention due to their rapid technological improvement and their potential to improve delivery methods of the healthcare industry. 
How does artificial intelligence stack up against experienced clinicians in the diagnosis of compression fractures?
This article will use a recent study published in The Spine Journal as a case study for use of AI in diagnosing vertebral fractures. Jun S. Kim, MD of Mount Sinai Hospital is an expert in the field of AI and using computer science to aid in the use of spinal surgery and orthopedics. He provides an in-depth assessment and explanation on the benefits and drawbacks of using this technology. 
A Framework for Understanding AI
Available open source AI options can be classified as low-level or high-level deep learning frameworks Though this is not official industry-recognized terminology, this classification provides an easier and more intuitive understanding of the frameworks. Low-level frameworks provide for a basic block of abstraction, flexibility and room for customization. High-level frameworks are used to further aggregate the abstraction. 
The purpose of this is to decrease human work. High-level frameworks limit the scope of customization and flexibility. High-level frameworks also use a low-level framework as a backend, often working by converting the data source into the chosen, customizable low-level framework for execution of the final model.1
Popular open source frameworks for clinicians and researchers include the low-level frameworks: 
TensorFlow  by Google (currently most popular; used in the Case Study below)
PyTorch  Geometric (developed initially by Facebook; easier learning curve than TensorFlow)
…and high-level frameworks:
Terminology
Artificial intelligence and machine learning comes with its own vocabulary. If you’re new to AI and ML, here are some of the most pertinent terms for this discussion: 
Deep learning (DL) is an AI machine learning method dealing with algorithms based on artificial neural networks that mimic the biological structure and functioning of a human brain combined with representation learning.
In shallow learning, input data is readily understandable and patterns are learned from the model. With deep learning, the model must learn meaningful representations of the data it’s been given, as well as learn parameters, all with the ability to generate classifications based on its learned representations.
The most common uses of deep learning are datasets involving images, text, or sound, because these contain data not readily interpretable by a computer – in effect, non-numeric.
Representation learning is a set of techniques the machine uses allowing it to automatically find the representations required for a feature’s detection or classification. Besides replacing manual feature engineering, representation learning allows machines to learn the features and apply them to perform a specific task.
Convolutional Neural Networks (CNNs) are deep neural networks that use a special linear operation called convolution, which is an important and distinctive element of CNNs. A CNN has a multilayer structure of a neural network, and is a DL algorithm developed based on animals’ visual functioning.
Convolution is a mathematical operation fundamental to common image processing. Convolution works by multiplying two arrays of numbers (usually of different sizes, but of the same dimensionality) in order to produce a third array of numbers of the same dimensionality.
A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers “stacked” between the input and output layers. Regardless of the type, all neural networks consist of the same components:
Neurons
Biases
Functions
“Medicine is complex and it doesn’t always lend itself to simple patterns, and thus can’t be modeled through shallow learning.” per Dr. Kim.
A neural network representation
Use of CNN in Detecting Osteoporotic Vertebral Fractures and Other Spinal Conditions
AI carries great potential for early detection of osteoporotic vertebral fractures (OVF). Standard OVF detection relies on computed tomography (CT) images. One argument for using CNN for disease and fracture detection is the potential to maximize diagnostic capability and minimize the human factors of subjectivity and errors occurring because of distraction and fatigue. However, this benefit has not been proven thus far, with available research pointing to a fairly equal diagnostic ability between humans and CNNs. 
According to Dr. Kim, CNNs “can be used to rapidly diagnose abnormal pathology; or, at the very least can change the priority at which they are examined by a physician. There are many different potential utilities of CNNs as it relates to spine.”
Potential uses of CNN in diagnosis of spinal conditions, scoliosis classification and identification of:
Implants
Stenosis
Using CNNs in Spine Surgery
Dr. Kim uses a combination of his own programming and tool others have built. “When I was a resident in training, I took an online course in machine learning as a means of pursuing and applying deep learning to orthopedics. There aren’t many orthopedic spine surgeons with domain expertise in machine learning, and most spine companies are making implants rather than software. More recently this has changed with the application of increasing technology in the operating room and in the perioperative period.” 
Using AI mainly for research, Dr. Kim posits, “More recently spine companies have pivoted and begun providing preoperative planning tools that use AI algorithms/neural networks to predict the correction a deformity or scoliosis patient will get after surgery. It can help you plan your fusion levels, your osteotomies, rod contour, etc.” Besides using CNN in diagnostic imaging, Dr. Kim also uses it for natural language processing (NLP), patient complication and prognostication (deep neural networks), and generative adversarial networks (GANs).
Can Using CNNs in Spine Imaging Translate into Better Patient Outcomes?
Dr. Kim believes these models will lead to better outcomes in the future. Currently though, “these models will require validation in the clinical setting before they become commonplace. I think where we can use CNNs is still being explored. At this point in time, I believe they may be safer to use as a tool to prioritize certain X-rays, CT, [and] MRI studies rather than classifying pathologies outright.” says Dr. Kim.
Since the beginning of his career, use of AI has changed Dr Kim’s approach to his practice of medicine. “It can change my surgical plan. There are preoperative planning tools that allow me to plan bony cuts and the contour of metal rods that I place during surgery to correct scoliosis or spinal deformity patients.”
Accounting for CNN Weaknesses 
CNN can be excellent in extracting features, such as edges, corners, etc. However, CNNs can be “brittle” – a term used by Dr. Kim to describe the propensity of a CNN to be unable to bend itself like a human in terms of interpretation. 
CNNs can only learn and adapt from the information they are given. “In CNNs, high-level details are done by high-level neurons. They check whether features are present or absent. They lose information about the composition or the position of the components. Humans have a much easier time with objects: 
Under different angles
Under different backgrounds
Under several different lighting conditions…
…because we have an ability to find signs and other pieces of information to infer what we are seeing. Thus, CNNs are brittle because they have great performance when the images they are classifying are very similar to the dataset on which they are trained,” but not outside those data sets. 
For example, if one hospital uses another hospital’s CNN dataset to try and detect fresh OVFs, the output will be skewed. The data set used is from another location using different imaging systems, different computers and different sets of patients with different demographics and conditions.
Other weaknesses include examples such as adversarial attacks, where adding noise to an image confuses the CNN. There are potential solutions to some of these issues with newer networks, like capsule networks and transformer visual networks.
Case Study: Promising Results from New Study Using CNN to Diagnose Fresh OVF
CNN is primarily used for detecting and classifying objects. A  2021 study  used CNN to create a diagnostic support tool for magnetic resonance images (MRI). The authors then compared the performance of two spine surgeons (20 and 7 years of experience) to the results of the tool. 
The method employed was a retrospective analysis of patient data from a clinical trial of patients that suffered fresh OVF. Patients were required to be over 65 and have a fresh OVF (defined as a fracture

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