# Machine Learning for Predicting Neutron Effective Dose
## 1 Introduction  
In the field of nuclear medicine, the use of radioisotopes for diagnostic and therapeutic purposes was introduced in the 1960s. Safety prerequisites and compliance with regulatory limits led to the need to pre-calculate the dose distributions in the target organ and surrounding tissues. To calculate the dose received by the internal organs, a detailed model was introduced to estimate the diagnostic or therapeutic dose for both the organ and the entire body. This work resulted in the creation of an MIRD phantom (Medical Internal Radiation Dose Committee) [1] in conjunction with the state-of-the-art Monte Carlo N-Particle Transport code (MCNP). This model considers the full body size and shape, as well as physical characteristics, such as the density and elemental composition of organs [2]: furthermore, it has the advantage of using less computational memory. During the 1980s and 1990s, a second type of anthropomorphic phantom--known as the voxel-based phantom--was developed. This phantom takes advantage of sectional tomographic images to better represent the shape and size of organs, and it can include smaller organs in moredetail. Additionally, a third type of phantom--known as the mesh-based or boundary representation (BREP) deformable phantom--was developed, which is also based on medical images. This phantom has higher resolution than the earlier phantoms and also possesses the ability to deform. All types of anthropomorphic phantoms are currently employed for research and operations in many radiological fields. Furthermore, all types have multiple versions available in different Monte Carlo codes, covering both gender and different age groups, as well as organ-specific models [3; 4; 5].  
The concept of effective dose was set out by the International Commission for Radiation Protection (ICRP) [6; 7]. It considers weighting factors for the selected organ tissue (radiation sensitivity) multiplied by radiation weighting factors associated with the radiation damage quality factor. The definition of the effective dose has great application potential, as it sets the dose limits for patients, workers, and the public [8]. Therefore, effective dose conversion coefficients (DCCs) have been calculated by employing all types of anthropomorphic phantoms and utilizing different Monte Carlo codes. This was achieved to accommodate the fact that defining an average person is difficult. Different locations use different types of population-based anthropomorphic phantoms [5] with the same elemental composition reference values [9; 10] and varying physical characteristics, in terms of total weight, height, organ size, and shape, against the ICRP reference model.  
Machine learning (ML) algorithms have been effectively applied in radiotherapy treatment planning [11], including for the segmentation and building of higher-resolution anthropomorphic tomography-based phantoms [5]. These applications depend on large amounts of evaluated data, increased computer capacity, and the development of powerful ML algorithms. One of the main advantages of ML is that it provides fast and accurate results, enabling rapid predictions [12].  
This pilot study attempted to predict organ doses using supervised ML methods through tabulating several published Monte Carlo calculations of neutron DCCs for different types of anthropomorphic phantoms. The construction of the used data set and the accuracy constraints of the ML models are presented, and the results regarding organ doses and effective doses for previously uncalculated anthropomorphic phantom data are compared to the neutron DCCs published by the International Commission for Radiation Protection (ICRP) [13; 14].