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Deep learning-based systems aid radiographers in prediction, decision-making

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Promising work is being performed in tissue classification and cancer staging, thanks to the advent of machine learning and other sophisticated models in diagnostic radiology.

Artificial intelligence, and deep learning in particular, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. However, the use of deep learning in radiotherapy prognostic modelling is still limited.

Deep learning is a subset of machine learning and artificial intelligence that has a deep neural network with a structure like the human neural system and has been trained using big data. Deep learning narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks.

The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaption, and image generation.

An article published in Clinical Oncology analyses 10 studies on the subject noting that researchers suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications.

It adds that the studies, however, demonstrate how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are several issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example, the translation of model developments into treatment plan optimisation that will require an additional combined effort from the radiation oncology and artificial intelligence communities.

Hence, the use of machine learning and other sophisticated models to aid in prediction and decision-making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas.

Envisioning the future of radiology

Dr. Ali Vahedi, Consultant Radiologist at Mubadala Healthcare, explains that AI has an important role to play in the current and future of radiology. At present, this is primarily in the form of helping clinicians improve efficiency and diagnostic capacity which is essential with the exponential increase of diagnostic tests conducted year-on-year. He adds that AI has the potential to rapidly evaluate a vast quantity of imaging data, helping to prioritise worklists and diagnoses, which will aid in reducing reporting time, improve the accuracy of reports as well as limit discrepancies. In addition, it will give radiologists more time for direct patient care and vital research.

Dr. Ali Vahedi, Consultant Radiologist, Mubadala Health Dubai.jpg

Dr. Ali Vahedi

A study published in the Progress in Medical Physics Journal highlights that high-quality simulated three-dimensional (3D) CT images are essential when creating radiation treatment plans because the electron density and anatomical information of tumours and OARs are required to calculate and optimise dose distributions.

Radiotherapy plays an increasingly dominant role in the comprehensive multidisciplinary management of cancer. As radiation therapy machines and treatment techniques become more advanced, the role of medical physicists that ensure patients’ safety becomes more prominent. With the advancement of deep learning, its powerful optimisation capability has shown remarkable applicability in various fields. Its utility in radiation oncology and other medical physics areas has been discussed and verified in several research papers. These research fields range from radiation therapy processes to QA, super-resolution medical image, material decomposition, and 2D dose distribution deconvolution.

According to Dr. Vahedi, the global imaging market size is expected to grow, driven by numerous factors such as growth in the number of hospitals and clinics and rising demand for minimally invasive surgeries.

He adds that rapid advancements in medical therapy also necessitate the need for regular multimodality imaging. In addition, technological advancements in medical imaging equipment are also contributing to the growth, with manufacturers introducing new products that are more compact, more cost-effective, and produce less ionising radiation than their predecessors. This improved affordability will invariably improve patient access to imaging. it can be concluded that over the past few years there has been a significant increase in both the interest in as well as the performance of deep learning techniques in this field.

Promising results have been obtained that demonstrate how deep learning-based systems can aid clinicians in their daily work, be it by reducing the time required, or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It remains to be seen when these techniques will be employed in routine clinical practice, but it seems warranted to assume that we will see AI contribute to improving radiotherapy soon. In conclusion, the application of deep learning has great potential in radiation oncology.

This article appears in the latest issue of Omnia Health Magazine. Read the full issue online today.

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