Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically ¹⁸F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from ¹⁸F-FDG PET/CT in the management of lymphoma patients.

Robotic surgery makes use of autonomous robots that can perform some surgical tasks on their own. Surgical robots performed well in conjunction with machine learning, particularly reinforcement learning (RL), allowing them to be used in complex environments, such as cutting a pre-determined pattern on soft tissue with surgical scissors and gripper. There is no doubt that soft tissue is deformable, so using a tensioning policy can determine appropriate tension direction from the pinch point at any time to have an accurate cut in the pre-determined trajectory. In this study, we used the deep reinforcement learning (DRL) approach to find an optimal tensioning policy for cutting soft tissues. In addition, we used an evolutionary algorithm with the operators appropriate to the problem and the learned tensioning policy to find the sequence of tensioning actions. The objective of this study is to determine the optimal tensioning policy and the best tensioning action sequence. The experimental results show that using the learned policy results in smaller damage and error and lead to the highest scores compared to the previous studies.

: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: “Depth of anesthesia", “Control of anesthesia delivery", “Control of mechanical ventilation and weaning", “Event prediction", “Ultrasound guidance", “Pain management", “Operating room logistic", “Monitoring", and “Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.

This paper examines the problem of querying beneficial constraints before clustering. Existing methods in this area choose constraints heuristically based on some prior assumptions on the usefulness of constraints. However, the usefulness and propagation of constraints are two important issues in the constraints selection that are not investigated simultaneously in most existing works. This paper addresses the problem of querying beneficial constraints using facility location analysis that is one of the most well-studied areas of the operations research. To this end, the source problem of querying ${k}$ beneficial constraints is transformed into an instance of target uncapacitated ${k}$ -facility location problem ( ${k}$ -UFL) and then is benefited from existing algorithms in the target space to find a solution to the ${k}$ -UFL problem. The solution to the ${k}$ -UFL problem is then transformed into a solution of the source problem of querying ${k}$ beneficial constraints. Both usefulness and propagation of constraints are achieved in this paper by respectively mapping them into the corresponding opening and service costs in target problem space and then minimizing the total cost in target space. The proposed method is based on an optimization framework and is entirely different from existing methods in the constraints selection that are limited to greedy approaches. A range of experiments is presented to compare the proposed method to alternatives and explore its behavior in the selection of clustering constraints.