Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way.

BACKGROUND: There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS: To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS: We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION: The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.

Optimization of dual electron multileaf collimator materials by use of EGSnrc.

A dual electron multileaf collimator (eMLC) has recently been proposed and ascertained to be effective in the collimation of therapy electron beams. The EGSnrc Monte Carlo code has been used in the optimization of the dual eMLC by simulation of the Varian 2100C medical linear accelerator with the applicator completely replaced by the dual eMLC, and calculation of the dose distributions in a water phantom. The planar fluence results showed that the material combination of 2-cm-thick brass and 2-cm-thick tungsten (eMLCT1) as upper and lower eMLCs, respectively, offers minimal radiation leakage outside the treatment field. Dose calculation results used in estimation of the maximum dose, depth of the maximum dose, surface dose, bremsstrahlung background, and penumbra indicate that eMLCT1 offers better beam qualities than do the other dual eMLCs that were considered. Maximum optimization was obtained with the dual eMLC designed such that the material of the lower eMLC had a higher density than that of the upper eMLC.