Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

Introduction. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia, a deadly white blood cell disease that impacts the human bone marrow. ALL detection in its early stages has always been riddled with complexity and difficulty. Peripheral blood smear (PBS) examination, a common method applied at the outset of ALL diagnosis, is a time-consuming and tedious process that largely depends on the specialists experience. Materials and Methods. Herein, a fast, efficient, and comprehensive model based on deep learning (DL) was proposed by implementing eight well-known convolutional neural network (CNN) models for feature extraction on all images and classification of B-ALL lymphoblast and normal cells. After evaluating their performance, four best-performing CNN models were selected to compose an ensemble classifier by combining each classifiers pretrained model capabilities. Results. Due to the close similarity of the nuclei of cancerous and normal cells, CNN models alone had low sensitivity and poor performance in diagnosing these two classes. The proposed model based on the majority voting technique was adopted to combine the CNN models. The resulting model achieved a sensitivity of 99.4, specificity of 96.7, AUC of 98.3, and accuracy of 98.5. Conclusion. In classifying cancerous blood cells from normal cells, the proposed method can achieve high accuracy without the operators intervention in cell feature determination. It can thus be recommended as an extraordinary tool for the analysis of blood samples in digital laboratory equipment to assist laboratory specialists.

Objectives: The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis. Methods: In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed. Results: The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR). Conclusions: Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

Introduction. While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method. Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results. We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion. Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.

Background: Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients. Method: A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline. Results: Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models. Conclusion: Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.