Cancer care in unarguably complex. Developing accurate and fast treatment plans is imperative, to say the least. Nonetheless, this has posed a challenge for clinicians. Unfortunately, they struggle to offer timely radiation therapy. According to one of the statistics, those who get delayed dosage suffer from the risk of recurrent or spread of cancer by significant mark. Intrigued by this, researchers at UT Southwestern Medical Center at Texas sought to leverage deep-learning technologies to address the problem. Moreover, its MAIA Laboratory had already stressed on such need for patients with lung, head or neck cancer in 2019. In the new study, the investigators took the scope of artificial technology (AI) ahead.
Medical Physics published the details of the study. Dose recalculations factoring in all imaging data and data from different medical teams go into making the optimal treatment plans. Moreover, with prognosis of the cancer, the treatment plans need repeated recalibrations. Amazingly, deep-learning models shortens this process.
Deep-learning Models Recalculated Radiation Dosage Faster and Accurately
In the study, the researchers fed data from 70 prostate cancer patients into the AI system. The system comprising four deep-learning model had recalculated dosages—accurately and quickly—before each dosage begun. The researchers precisely hoped to look at the change in anatomy of the patients undergoing radiation therapy. Further, they generated Pareto optimal dose distributions with the help of the neural network.
Usually, clinicians take at least several minutes to factor in the change in anatomy. But with an AI algorithm, the study found the task easier and whole lot faster.
Reiterating on the findings, one of the researchers asserted that the deep-learning models could simplify the work of doctor and the dosage planner. The 3-D renderings, believe the scientists, could help make the best treatment plan for cancer patients. Stridently, the MAIA’s seeks to leverage the combined human and learned domain knowledge on improving other facets of health care.