How deep learning algorithms can improve surgical accuracy and efficiency
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Spinal radiography: How deep learning algorithms can improve surgical accuracy and efficiency

Deep learning and AI-based systems are revolutionizing healthcare and biotechnology around the world. They have the potential to detect anomalies and critical triage cases and predict outcomes much faster and more accurately than seasoned experts using standard imagery.

Spinal radiography is one area where deep learning can help advance medicine significantly and be a valuable tool for health and care staff.

Due to the complicated and often obscure nature of spinal conditions, standard imaging procedures such as MRIs can often be too time-consuming and inaccurate to conduct in a primary care setting. This is where deep learning and AI can help.

What is deep learning?

Machine learning (ML) is a subset of artificial intelligence (AI). It allows computers to form their knowledge and behavior without being explicitly programmed. Deep learning is a subset of ML where artificial neural networks (ANNs) emulate how a human brain learns new information.

ML allows self-driving cars to detect the difference between a pedestrian, a lamp post, a traffic light, and a full moon in the night sky. It’s also what systems like Google and Alexa use to understand human language or find meaning in billions of data points.

ML can find anomalous patterns in MRI and CT scans in the medical field that only a trained human expert can spot. For example, research indicates that AI-assisted radiologists can reduce false negatives in cancer screenings.

Detection of carcinomas, liver cirrhosis, rare diseases, and bone fractures in X-ray images is positioned to increase speed and accuracy thanks to these emerging technologies.

Computer systems can use information like a patient’s symptoms and demographics to predict pathologies, recommend treatment and hospital release procedures, and predict survival time in terminal cases.

One type of ANN is the convolutional neural network (CNN). It’s programmed like the human visual cortex and heightens the ability to detect image patterns. It does this by filtering or “convolving” raw input data to find thousands of visual features to classify the image.

This allows it to scan a fingerprint to determine someone’s identity or find dogs in a photograph of multiple types of animals.

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A typical ANN requires that features like limb length, postural dimensions, nose width, and eye shape be explicitly specified. On the other hand, a CNN will automatically find these using advanced technology.

How deep learning can improve spinal procedures

AI is one of many assistive technologies disrupting cleanroom facilities. For example, computer-assisted imaging, navigation, motion capture, robotic surgery, digital X-ray machines, and in-surgery augmented reality (AR) empower hospital staff.

Here are some ways deep learning has been successfully implemented in spinal care:

Using deep learning for image processing and diagnosis

Deep learning in healthcare aims to reduce inaccuracies and streamline otherwise time-consuming manual tasks. While tools like a healthcare CRM can accelerate operational efficiency, AI can complement processes by augmenting certain tasks or assuming them entirely. Let’s look at how self-learning software works its magic.

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Data collection

Deep learning starts with access to a data pool. The sheer volume of images is what fine-tunes its accuracy. It isn’t uncommon for it to analyze thousands or even tens of thousands of scans in a single study. Fortunately, major hospitals produce hundreds of scans every day. So there’s no shortage of images to help train the algorithm.

Data preprocessing

Basic operations like resolution, cropping, rotation, sharpening, and contrast are performed on the image batch to improve results. Of course, the AI can do these operations internally if it leads to more reliable results, but preparing the images for the AI before feeding them to it can give the computer a leg up.

Doing this beforehand also ensures the variables tagged with the images are standardized, so all images are equivalent data-wise.

Image segmentation

Here, the AI pinpoints separate objects — like a cat vs. a dog vs. a tortoise — in the image and outlines them with a boundary.

Feature engineering

Features are small distinguishable motifs that occur in an image. Feature extractors simplify raw pixels into representative data by finding those shared across the dataset.

In feature selection, features are iteratively combined, abstracted, and cast away to keep only the most useful ones.

Model selection

DNNs and CNNs are popular now, but classic techniques like Random Forest, k-nearest-neighbor (K-NN), support vector machines (SVM), decision trees, and naive Bayes classifiers may do just as well.

The one depends on factors like dataset size, feature depth, task complexity, maintainability, and available computing power.


In its training phase, the system learns to produce accurate results — not only for the current image repository but also for data to be presented in the future.

To do this, the dataset is split into three different sets:

    1. Training set for developing ML models.
    2. Cross-validation is set to score and select the best model.
    3. Finally, a test is set to predict outcomes.

Here the data scientist tweaks the model’s internal knobs in hyperparameter tuning. The number of neurons, activation function, learning rate, hidden layers, and iterations (epochs) are examples of hyperparameters in a DNN. This prevents errors like overfitting and underfitting.

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Model evaluation

Here, the data engineer decides the form the answer from the ML model should take. For example, it’s not enough to know how many subjects have spinal deformities. You also want to learn the true and false positives and negatives ratio.

With the chosen metrics comes an appropriate visualization chart. For example, a confusion matrix is a colored table that shows the response classes. Other options include graphs like the Area Under Curve (AUC) and Receiver Operating Characteristic curve (ROC).


The model is ready for operation. However, remember that the quality may shift over time because new data may vary from the dataset used in the initial training. So it’s important to keep monitoring the model for potential retraining and redeployment.

AI’s role in the future of spinal care

While AI can execute high-level medical tasks, it can’t comprehend the impact of a wrong decision. For example, although human doctors understand “scanxiety,” many patients experience medical scans and the human cost that can result from a mistake; computers are, of course, robotic.

An artificial brain can also develop biases regarding demographics, just as a human brain can. Computers can even display “reward hacking,” where they manipulate the system to get around its initial intent so they can be rewarded for good behavior.

Because AI doesn’t always do what it’s instructed to, it can be challenging to accept AI as part of the workforce. And in many cases, human experts are still better at spotting certain conditions. However, AI can be a valuable second opinion. It can also preserve the total biopsychosocial picture of the patient’s case.

Technology is at a crossroads moment. There are heaps of data, but it can be difficult for humans to make sense of it. Deep learning can analyze larger datasets and provide additional information to speed up the referral process. AI is reliable across all medical scans, even those prone to human error.

This saves time for both doctor and the patient. It can help the patient get the right treatment faster, and it can help reduce the $110 billion annual expense of spinal care in the U.S.

It’s unlikely that we’ll see a fully autonomous synthetic surgeon anytime soon, but deep learning AI will streamline the spinal care field and play an essential role in preventing misdiagnosis.