The advantages of CT have led to a significant increase in its use as they produce fast image acquisition in high quality. Conversely, the risk of contracting cancer due to absorbed radiation dose during imaging procedures is of major concern to patients of all ages, including children and pregnant women (1, 2). In addition to accurately estimating the patient radiation dosage, it is important to examine the possible absorbed radiation dose during CT examinations for patients. Clinical studies in X-ray radiographic imaging have demonstrated that radiation doses to patients can be significantly reduced without compromising patient diagnosis. It is expected that radiation doses to patients would be reduced by half if the patient's body thickness is decreased by 1 cm, in comparison with current clinical practice, without compromising patient diagnosis. Zheng et al. derived a general equation for the optimal selection of scan parameters in X-ray imaging, including CT and radiographic imaging (3). The radiation dosage the patient receives depends on several factors, including the type of CT system and the acquisition process used. There is a considerable effort in reducing radiation doses to patients whilst maintaining accurate diagnosis in CT imaging worldwide (4–6). Reported evidence was drawn from phantom studies that involve newborns as well as adult participants. Such phantoms have been often used in dosage measurements of the patients' dosimetry in diagnostic radiography (7). Another method for CT dosimetry is known as the Monte Carlo simulation method. The expression “Mathematical phantoms” is used to stand in for the ordinary Monte-Carlo patients. The number of mathematical phantoms has doubled in the previous decade. Additional scanners and patient-specific modeling models have been employed to arrive at an accurate dosage from CT studies (8). Using an ordinary Monte-Carlo software package to measure individual patient-specific radiation doses could be needed. Monte Carlo simulation is highly effective for dose prediction. It is a time-consuming process and operators must have the knowledge to carry out the CT scanner tests, but they are not necessary to simulate CT scanners (9). artificial neural networks (ANN) offer a flexible approach to aid physicians and researchers in dealing with large amounts of complex medical data (10–15). The literature verifies that some previous studies were performed in terms of utilization of Artificial Intelligence (AI) in diagnostic radiology. Meineke et al. used a machine-learning algorithm to assess dosage optimality in CT efficiency (16). In another paper, Sinha et al. studied the use of artificial neural networks in pediatric radiographs with regard to their potential in the identification of CT findings (17). They found that the ANN model is more sensitive (82.2%) than physician estimation (62.2%). Their results showed that with dosage optimization, machine learning (ML) would comprehensively and rapidly detect CT scans (17). It may be used to further automate CT quality assurance. McCollough and Leng investigated the application of artificial intelligence in CT dose optimization (18). Their results showed that AI-based techniques that automate the organ and pathologic characterization had been safely transported from testing to clinical practice, boosting the justification for medical CT scanning on the patient's side. To the best of our knowledge, DLP estimation using 10 different scan-related input parameters for Artificial Neural networks (ANN) for abdominal CT scans was not performed in the literature. Therefore, we sharply focused on generating a novel dose-length-product (DLP) estimation algorithm using advanced artificial learning methods. The Artificial Neural networks (ANN) are used to estimate the DLP compared to the ordinary methods based on real patients' parameters and the results are compared and validated. We proposed that Artificial neural networks (ANN) can assist in CT quality assurance in identifying optimal doses and have already been missed by current quality assurance methods. The authors believe that the integration of outcomes will improve the knowledge gap in DLP estimation when patients and protocol parameters are known and complete. Although there is a close relationship between CTDI and DLP values, technological issues and software mistakes that arise in DLP values in everyday clinical practice interrupt scientific investigations and create some difficulty in understanding. The purpose of this research is to assess the DLP values that will be utilized during the various studies, particularly across hospitals, in light of the algorithm produced by evaluating the reliability. Thus, DLF values for data sets collected from different centers may be recalculated using this approach, and any quantitative disparities can be highlighted to offer a better understanding of the present technical challenge.

In this study, different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of accuracy of training data. We considered the same amount of training, validation, and test data sets as 70, 15, and 30%, respectively. Figure 1 shows the structure of the generated ANN model along with the information on input layer parameters. Input data are received from 551 patients with 10 features namely patient weight (kg), patient size (cm), BMI, mean CTDI volume, scanning length, KVP, X-ray tube current, exposure time, exposure time per rotation, and pitch factor, respectively. A randomized selection has been used for 385 (70% of N) training data, 83 (15% of N) validation data, and 83 (15% of N) testing data (see Figure 2A). In this study, twelve neurons were utilized in the hidden layer of the learning algorithm. Accordingly, we used a sigmoid-transfer function in each neuron due to its non-linear behaviors. The structure of the modeled learning algorithm in MATLAB-NF can be observed in Figure 2B. The learning factor was set to 0.04 and the momentum factor to 0.1. Figures 3A, 4A, 5A show the outputs of training data sets as a function of actual (target) DLP obtained by the Levenberg-Marquardt algorithm, Bayesian algorithm, and Scaled-Conjugate Gradient algorithm, respectively. Using training data in ANN programs is a straightforward term, but it is at the heart of these techniques. They imitate the human brain's ability to weigh complex stimuli and help produce corresponding decisions in the neurons. In this study, basic training data were used to assist the algorithm in learning how to implement neural network technology to estimate DLP values (output). We reported the R-values of training data as 0.88403, 0.91633, and 0.76938 for the Levenberg-Marquardt algorithm, Bayesian algorithm, and Scaled-Conjugate Gradient algorithm, respectively. The maximum R-value for the overall training process (R = 0.91633) was reported for the Bayesian algorithm. On the other hand, we used test data to check the learning process after it has been set to train on an initial dataset. The theory is that predictive models will still need to be confirmed by use. The test data is the only algorithm that checks the ANN algorithm's overall precision in a purely quantitative manner. In this study, the test data collection from the Levenberg-Marquardt algorithm, Bayesian algorithm, and Scaled-Conjugate Gradient algorithm is also used for the ANN model validation. The meaning of test data in AI applications is highly important since the abilities of a designed algorithm can be observed during the testing of prediction as a function of existing values. Figures 3B, 4B, 5B depict the correlation of test data as a function of a target value. We reported the R-values of test data as 0.92561, 0.78572, and 0.85457 for the Levenberg-Marquardt algorithm, Bayesian algorithm, and Scaled-Conjugate Gradient algorithm, respectively. The maximum R-value for the overall training process (R = 0.92561) was reported for the Levenberg-Marquardt algorithm, which provided an equation for the output as 0.8^*Targe + 2.1e+02, where the target is the real and observed value. Finally, fifty independent examinations were checked in terms of accuracy in the obtained learning algorithm in Levenberg-Marquardt. However, it‘s worth mentioning that the correlation of all learning algorithms was reported above 0.78 (see Figures 3C, 4C, 5C). Figure 6 shows the obtained Simulink by the Levenberg-Marquardt algorithm. The correlation of the Levenberg-Marquardt algorithm for all data was also reported as R = 0.88771. The ten different input parameters were defined in the constant x1 box of obtained Simulink. The obtained results showed that the provided algorithm is able to estimate DLP values with high accuracy. As a result of this study, it can be said that the Levenberg-Marquard-based learning algorithm, which was created with 12 hidden layers and 551 abdominal CT scan data (each exam contains 10 different scan parameters), provides the highest level of prediction rate at 0.92561.

This study showed that it is possible to do a large-scale CT dose analysis for DLP values in abdominal CT scans using the ANN model. This is a comprehensive study that shows the importance of AI methods when applied to a large number of CT exams with their meaningful inputs and accordingly output. It is well-known fact that the recommended CT dosage is calculated as dose index (CTDIvol) or dose length product (DLP) (22, 23). Therefore, the main output was considered DLP value, whereas the potential parameters that can affect the DLP values were patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp mAs, and exposure time per rotation, and pitch factor. The study of how the brain operates can also lend itself to using neural networks, as the layers in the networks mimic the various processing stages of the brain and how it uses data can be studied in almost the same manner. A neuron is connected to the layer above it. The single-layer method results are computed as the product of each input unit being joined with the next using a linear operation, which connects these two neurons in a row in the hidden layer, e.g., with non-linear activation functions, when in the regression model. It has been found that network-based regression models can yield better results than the latter, as a result of a discovery of wider use. We conducted ANN to evaluate the possibility of using DLP prediction on a wide sample as part of CT assurance for results. The term of dose assessment for risky patients has long been a significant concern and numerous systems for managing community doses have been developed. Since the AI used for CT dose optimization involves the use of several scan parameters, we believe that this approach is an even larger number of scans. The literature review showed that many novel studies were performed in terms of the utilization of AI in different sub-areas of radiological sciences (16, 17, 24–28). However, to our understanding, no study has attempted to examine the usefulness of the ANN algorithm with comprehensive learning models in abdominal CT scans yet. Therefore, our study provided not only a high accuracy prediction on DLP values but also evaluated different learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient, comparatively.

The main limitation of our investigation was to perform retrospective analysis, but this is very important when assessing radiation dose as it is impossible to experiment on humans. However, a large number of CT scans, along with their input parameters, enabled the neural networks to work with high efficiency. In clinical routine, DLP is a value that can be obtained as a result of a CT scan. It means that pre-assessment is physically challenging for patients in terms of risks from ionizing radiation. Since we obtained a high accuracy DLP estimation in our novel algorithm, one can say that a risky patient can be evaluated before a potential abdominal CT scan. The current investigation obtained a highly accurate DLP estimation that can be used to assess the radiation dose for the risky patient before an abdominal CT scan. Absorbed x-ray radiation can destroy molecules and introduce new mutations. A high level of radiation damages human tissue, as shown by the degree of skin burns and the likelihood of cancer. Since a higher dosage is often associated with cancer, it is therefore expected that a lower dose would be, too (29, 30). There is actually no direct research evidence that shows that low radiation levels (such as those used in imaging) have an increased risk of cancer (31, 32). Therefore, any type of risk assessment prior to CT examination would be beneficial for patients in terms of the ALARA principle. The outcomes of the recent investigation can be considered as a practical tool and reflection of promising artificial intelligence methods for diagnostic radiology routines. Moreover, this type of practical approach can increase the awareness of radiographers on artificial intelligence in addition to dose reduction strategies, while they are in an adaptation process on involvement in the future's radiology (33, 34). In summary, ANN will comprehensively identify DLP values in abdominal CT scans that could benefit from dose optimization. It can help streamline the process of CT quality assurance, making dose data more extensive and less time consuming.

In this retrospective, cross-sectional study, the goal was to test Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during abdomen CT scans for quality assurance. The ANN model was built with several input factors in mind, including patient weight, patient size, BMI, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. As retrospective data, the aforementioned examination details of 551 abdominal CT scans were employed. The correctness of the training data was tested using several learning methods such as Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient. For the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, the R-value is the correlation coefficient for the real system, and the system output is 0.925, 0.785, and 0.854, respectively. The researchers discovered that the Levenberg-Marquardt algorithm accurately detects DLP levels in abdominal CT scans. It can be a good way to make CT quality assurance easier. It can be concluded that the results of this innovative artificial intelligence system can be employed for high-precision DLP estimations before abdominal CT tests, especially when radiation-related risk variables are high or several CT images are required for ALARA risk evaluation. Similarly, artificial learning approaches may be concluded to be powerful tools that can be employed for many forms of radiation-related risk evaluations for quality assurance in diagnostic radiology.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

HT, HZ, SI, and MA: wrote the main manuscript text. WE, FA, TE, and AE: prepared and drown all figures. All authors reviewed and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

The article processing charge was funded by “Dunarea de Jos” University of Galati, Romania.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.