Machine Learning in Medical Imaging: Powering Diagnosis

Medical imaging is an essential and widely used tool to recognize and treat diseases across the globe. It is used to create a visual representation of the inside of the body and requires experts to interpret them. Medical images such as X-rays, MRIs, CT scans, and ultrasounds are the first step of diagnosis in many cases. They help medical professionals to identify and visualize anomalies in the body.

However, the involvement of humans to read medical images and identify the ailment often leads to errors. Sometimes, the diagnosis is not done at the right time, causing more pain and suffering to the patient. While sometimes, incorrect diagnosis directs to incorrect treatment, worsening the patient’s condition. Hence, the need for accurate and efficient analysis of medical images is more critical and required than it sounds.

Moreover, the amount of medical image data being generated has increased as people have become more conscious about their health post-pandemic. It is not easy and manageable for any human individual to go through all of those images fast. The process will take its time, else it is bound to have errors. So, the urgency and need for a more robust solution for medical imaging is high.

Recently, artificial intelligence and machine learning have emerged as vital tools that can assist in medical imaging. Machine learning algorithms can analyze medical images, recognize patterns in them, and diagnose diseases much faster and more accurately. Medical professionals can use them to support their treatment decisions and ensure the patient is taken care of as soon as possible.

Different Applications of Machine Learning in Medical Imaging

Image Segmentation

Medical images have representations of different types of tissues and structures. Medical professionals need to look at each tissue and structure separately to understand and study them. However, when dealing with complex images, this can take a lot of time.

Machine learning in medical imaging can save a lot of time and resources by segmenting the images into relevant tissues and structures. This process is known as image segmentation. Using it, medical professionals can examine tumors, blood vessels, and other important systems in the body much more easily.

Disease Diagnosis

Medical images are used in the diagnosis and treatment of many diseases and conditions. As already discussed, the involvement of humans to decipher these images leads to erroneous diagnoses or delayed treatment.

By using machine learning for medical imaging, radiologists can diagnose and classify diseases in medical images with greater accuracy and speed. The ml models trained on relevant medical image data can detect and classify diseases in medical images. The results can help radiologists reach to conclusions faster, leading to immediate treatment and better patient outcomes.

Image Registration

The process of aligning and modifying several images of the same patient to obtain a complete view is known as image registration. Using machine learning for medical imaging, image registration can be achieved easily.

ML-based image registration is used in motion correction, inter-subject registration, and intraoperative image registration. Machine learning increases the accuracy and efficiency of medical image registration. It helps in disease progression and therapy effectiveness monitoring.

Image Reconstruction

Image Reconstruction is the process of creating high-quality images from incomplete or degraded data using machine learning. It is useful when image quality is compromised to reduce radiation exposure and high-quality images have to be rebuilt from sparse data. It is also useful when image quality is degraded due to motion artefacts and reconstruction without motion is desired.

Many medical applications use it to obtain high-quality medical images for clinical use at the lowest cost and risk to patients. In fact, medical images such as X-ray CT, SPECT, PET, and MRI are often reconstructed to improve their quality and diagnoses. The machine learning models reduce noise, improve image resolution, and enhance image contrast for better visuals.

Importance of Machine Learning in Medical Imaging

Machine learning has already had a big impact on medical imaging and will continue to define the field’s future. The importance of machine learning in medical imaging is huge, as it leads to the following benefits:

1. Early Diagnosis

Machine learning can easily recognize and identify difficult-to-diagnose diseases and disorders. It also detects cancers that would be difficult to identify in the early stages of other inherited disorders. So, early treatment can be given to patients and their suffering can be minimized.

2. Improved Accuracy

Machine learning algorithms can improve the accuracy and sensitivity of medical imaging. It can easily find small patterns or changes in pictures that are not evident to the naked eye. This can aid in the earlier detection of diseases, resulting in more effective therapies and better patient outcomes.

3. Faster Imaging

The use of machine learning in medical imaging makes the capturing, processing, and analysis of medical images fast. This saves time for both patients and doctors, as prompt diagnosis leads to immediate treatment. It also allows more patients to be taken care of in a day as radiologists can obtain results and deliver them quickly.

4. Improved Quality

It becomes easy for the radiologist to comment on the medical images when they are of high quality. The quality of medical images is improved with the help of machine learning algorithms. They perform noise reduction, motion removal, and distortion reduction to provide better, sharper, and more accurate images. Using them, more useful observations are provided by the radiologists.

Challenges and Limitations of Machine Learning in Medical Imaging

Although machine learning has great potential in medical imaging analysis, it has several challenges and limitations to address. The major challenges are mentioned below:

1. Data Complexity

Machine learning requires significantly large and high-quality data to train algorithms. It is so important because the performance of machine learning algorithms is greatly dependent on the quality and quantity of data used for training.

Often, the medical images contain noise, artefacts, and inconsistencies that can impact the accuracy of the algorithms. So, they need to be removed or reconstructed prior to training. Besides this, collecting and labeling complex datasets can be time-consuming and expensive.

2. Lack of Standardization

Machine learning algorithms trained on one dataset may not perform well on another. This is due to the existing differences within image protocols and population characteristics. It is difficult to develop a universal model that can perform well on all datasets. The accuracy of the models can affect the lack of standardization in the way medical images are acquired, stored, and annotated.

3. Interpretability

Machine learning algorithms are difficult to understand. It is hard to interpret the decision-making process, gain trust in the algorithms and incorporate them into clinical decision-making. Healthcare professionals don’t accept them as they need to be able to trust and understand the decisions made by models.

4. Biases

Machine learning algorithms can be biased, which reflects the inclination of the training data toward a specific type of disease or disorder. It can lead to inequalities in health care and perpetuate existing stigma and inequalities. Besides this, it can identify well only a particular type of disease or disorder, reducing reliance on it.

5. Legal and Ethical Concerns

There are ethical regulatory issues and corners with respect to the use of machine learning in medical imaging. All of it comes down to the appropriate use of patient data for training models and efficient outcomes that support the medical field. Because of these insecurities, patients hesitate to share their medical imaging data and don’t support the use of machine learning in diagnosis.

Ongoing research and development will ensure that these challenges and limitations are overcome, paving the way for the use of machine learning in medical imaging across the globe.

Machine Learning Is Changing How We Look At Medical Images

The use of machine learning in medical imaging promises to improve patients’ healthcare and treatment proceedings. However, the development process has faced a number of systematic challenges such as annotation, biases, data quality, data collection, ethical concerns, etc.

Yet, we cannot deny the fact that machine learning performs on par with medical experts to diagnose various conditions from medical images. Sooner or later the machine learning-based medical imaging solutions will gain acceptance and get certified for clinical use.

If you have any requirements for AI/ML solutions related to the medical and healthcare industry, Anubrain’s experts can help you. Contact us today!