Taking Orthopedic Image Analysis One Step Further with Machine Learning

Yana Yelina
Yana Yelina -

Implementing Machine Learning (ML) in medical image analysis isn’t new. Radiologists actively leverage automation tools to significantly improve every step of the medical imaging pathway. This includes image acquisition and reconstruction to its analysis and interpretation.

The results of ML-powered image analysis are crucial for solving critical challenges like diagnostics and treatment planning across several healthcare fields, including cardiology, pulmonology, and ophthalmology. Orthopedics is no exception. From broken bone reconstruction to joint segmentation to cancer identification, machine learning helps orthopedic surgeons accelerate the shift to value-based care.

Bone Alignment Powered by 3D

According to the World Health Organization, up to 25% of patients suffer from surgery complications. Furthermore, one million end up dying during or after the operation. To alleviate this tough problem, physicians should strive to enhance the accuracy of image analysis and, consequently, surgery planning. ML steps in to assist them with that.

In orthopedics, creating 3D models of a patient’s anatomical parts is crucial to guide surgeons during the procedure. However, it might be challenging to reconstruct a surface from a sparse set of points. For example, when a patient has a fracture in long bones or lower limbs.

In this case, there’s a need for the initial alignment of the bone parts. And a computer vision-powered system can spare surgeons the need to do it manually, thus avoiding minor segment misalignment. The resulting virtual model will provide critical guidance for the surgery or, if needed, for implant design by indicating the exact bone position and orientation.

Accurate Detection of Bone Cancer

An effective tech tool within oncology, machine learning can also be used to identify osteosarcoma, the most common bone tumor. Although not so common as other cancer types, metastatic bone malignancies might appear following breast or prostate cancer. And early detection of these skeletal metastases notably contributes to specifying the prognosis and personalizing treatment.

Early cancer identification starts with computer vision-fueled skeletal segmentation and its separation from surrounding anatomical parts in a 2D format. Then, successive 2D images are automatically stitched into 3D surfaces of bones and other skeletal-related structures. All this makes it easier for ML to locate abnormal regions both near cartilage and in the bone and define skeletal regions at high risk of fractures.

The detected metastatic lesions are further classified according to severity levels through Support Vector Machine-based (SVM) algorithms previously trained on a set of manually classified normal and abnormal lesions. After that, physicians can proceed to immediate treatment, increasing survival rates among cancer patients and improving their quality of life.

Automatic Bones and Joints Segmentation

As you could understand from the previous section, segmentation plays an important role in medical image analysis. Organ measurement, isolation of organs from tissue, cell counting — artificial intelligence can automate these and other mission-critical tasks. ML-fueled segmentation is used in orthopedics for precise bone and joint examination, knee and hip replacement planning, lesion detection, shoulder surgery preparation, and other procedures.

Of course, to get fair results, you can use some out-of-the-box solutions, but tailored ML analysis will help you handle the most daunting challenges. Among them is image degradation due to metal artifacts. Previously trained on synthetic data produced from a simulation-based analysis, a machine learning-enabled system can boost the results of real-time orthopedic image processing.

Besides heavily degraded images, ML automation can also effectively analyze images featuring osteophytes, missing cartilage, or merging bones — by focusing on the anatomical locations that are more prone to algorithmic errors. And to ensure precision in bone segmentation on a pixel level, you can leverage sophisticated classical algorithms to perform post-processing.

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A leap forward in the orthopedics area, ML-powered image analysis is set to revamp diagnostics, enable hyper-personalized treatment, increase survival rates for patients with terminal diseases, and accelerate recovery time.