Reducing Diagnostic Noise With AI In Orthodontic Practices

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Generally, noise refers to unwanted, irregular granular patterns present in a diagnostic image (such as a radiograph). It is a common phenomenon that degrades the image information leading to consequences. Research suggests that noise corrupts the information and renders the images useless. Comprehension from a noisy radiograph is also distorted. Therefore, it is important to minimize noise. There exists another type of noise i.e., noise in the decision-making of an orthodontist. This noise can lead to altered decisions and treatment plans. This article discovers the consequences of noise in orthodontic imaging.

Consequences Of Noise In Radiographs

Faulty X-rays can pose different types of problems in a healthcare setup. Improper evaluation of medical/dental radiographs can lead to delayed or even misdiagnosis. This may ultimately lead to over/under-treatment, incorrect treatment, or poor outcomes. As a consequence, practitioners also have to face dissatisfied patients which can negatively impact the clinician’s reputation and career.

The most common types of noise present in dental X-rays are Gaussian and Poisson noise, leading to significant image deterioration. Researchers have long identified the negative impact of noise on X-ray analysis and treatment planning. Therefore, different techniques have been employed to minimize it. Previously used techniques involved traditional and hybrid methods of denoising but now we are seeing a shift towards deep learning techniques (of AI).

Denoising Dental X-Rays With Artificial Intelligence

AI is constantly evolving and with every iteration, we see applications in a newer field. Nowadays, Large Language Models (LLMs) like ChatGPT4 and Llama are gaining wide popularity for fitting in a variety of fields. It provides you answers for anything you throw at it. Similarly, Convolutional Neural Networks (CNNs) are deep learning algorithms offered by AI that are continuously being used to improve dento-maxillofacial radiology (DMFR).

How Does AI Reduce Diagnostic Noise In Orthodontic Practice?

To understand how AI denoises an image, it’s important to know about diagnostic images. The three fundamental components of a radiographic image include:

  • Sharpness
  • Contrast
  • Levels of noise

What makes noise correction complex is the fact that noise and sharpness are intertwined. Traditional methods of noise reduction introduce blurring to the image. This removes the graininess but at the same time, it degrades the image quality and sharpness. The lack of sharpness and detail adds to further noise and masking of important anatomical structures. Thus, the more denoising is applied, the greater the noise we get. Scientists were stuck in this vicious cycle until the advent of Artificial Intelligence.

AI uses convolutional neural networks for deep learning of medical images. The process of learning from prior information is called regularisation. Based on the multiple data provided to it, AI is capable of reproducing/reconstructing the images with better output.

The most common methods of image reproduction include:

  • Encoder-decoder architecture: Here a neural network takes an image (input) and with the help of computation, downsamples it, before sequentially upsampling it. Examples include AiCE and TrueFidelity.
  • Generative adversarial networks (GAN): They generate normal CT images from low-dose CT images without smoothing the image (like encoder-decoder).

The end result of these AI techniques on noise-ladden dental radiographs is:

  • Reduced noise
  • Enhanced contrast
  • Improved resolution

A better visualization of the structures can allow better interpretation and timely and accurate diagnosis.

The Magnificent World Of Carestream

Carestream is an AI-based solution to reduce noise in X-rays. This AI-based smart noise cancellation (SNC) is approved by the FDA and has been put to good use by orthodontists.

With smart noise cancellation (SNC) experts have successfully differentiated noise from sharpness (ending a years-old dilemma!). The deep convolutional neural network in this system is trained to identify and predict a noisy area in the input image. The system has already learned and trained from a plethora of low-noise and high-noise images (from patients, cadavers, and phantoms). Based on the vast number of images provided, Carestream is able to identify areas and levels of noise and then reproduce images that are evidently clearer than standard processing.

Has AI Been Up To The Mark?

Studies show that AI does a fantastic job of improving image quality in CT staging. Another study revealed that AI processing effectively addresses dental radiographic (bitewing X-ray) issues like noise, contrast, and sharpness. This improves the reliability of automated diagnosis in dentistry.

Carestream is known to retain spatial details and there is no notable degradation of the anatomical sharpness. When paired with the SmartGrid software, Carestream provides a better contrast-to-noise ratio.

Clinical research shows that landmark identification on orthodontic X-rays is not good after conventional noise reduction processing. A 2021 study showed that the application of AI on oral and maxillofacial radiology has many positive effects, one of which is accurate noise reduction. Therefore, it can safely be concluded that Artificial Intelligence can effectively reduce noise in orthodontic practice.

Final Word

Dental X-rays play a crucial role in an orthodontist’s practice. Landmark identification on a lateral cephalogram lays the foundation for analysis and orthodontic treatment. Noise-laden dental X-rays lead to delayed, improper (under or over), and inaccurate treatment with poor outcomes. Conventional methods of denoising X-rays have numerous shortcomings. Therefore, experts have endowed this role with artificial intelligence. AI (deep learning) is fed a myriad of images from patients, cadavers, and phantoms. With the help of convolutional neural networks (CNNs), the system identifies areas and levels of noise in the input image. Then, it reconstructs the image (based on prior knowledge) by identifying the important anatomical landmarks with better contrast and enhanced resolution. Carestream is the latest smart noise cancellation (SNC) system highly liked by clinicians around the globe. Orthodontists can make good use of this FDA-approved system to improve image quality and automated diagnosis efficiency.

References

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