Towards realistic laparoscopic image generation using image-domain translation

Background and ObjectivesOver the last decade, Deep Learning (DL) has revolutionized data analysis in many areas, including medical imaging. However, there is a bottleneck in the advancement of DL in the surgery field, which can be seen in a shortage of large-scale data, which in turn may be attributed to the lack of a structured and standardized methodology for storing and analyzing surgical images in clinical centres. Furthermore, accurate annotations manually added are expensive and time consuming. A great help can come from the synthesis of artificial images; in this context, in the latest years, the use of Generative Adversarial Neural Networks (GANs) achieved promising results in obtaining photo-realistic images. MethodsIn this study, a method for Minimally Invasive Surgery (MIS) image synthesis is proposed. To this aim, the generative adversarial network pix2pix is trained to generate paired annotated MIS images by transforming rough segmentation of surgical instruments and tissues into realistic images. An additional regularization term was added to the original optimization problem, in order to enhance realism of surgical tools with respect to the background. Results Quantitative and qualitative (i.e., human-based) evaluations of generated images have been carried out in order to assess the effectiveness of the method. ConclusionsExperimental results show that the proposed method is actually able to translate MIS segmentations to realistic MIS images, which can in turn be used to augment existing data sets and help at overcoming the lack of useful images; this allows physicians and algorithms to take advantage from new annotated instances for their training

Use of hormones in doping and cancer risk

Hormones with anabolic properties such as growth hormone (GH) and insulin-like growth factor-1 (IGF-1) are commonly abused among professional and recreational athletes to enhance physical ability. Despite their adverse effects are well-documented, the use of GH and IGF-1 has recently grown. This article highlights the anabolic activity related to mechanisms of cancer development and progression. GH/IGF-1 axis is able to activate cellular mechanisms that modulate every key stage of cancer formation and progression, such as inhibition of apoptosis, resistance to treatments, and induction of angiogenesis, metastatic process and cell proliferation. Results from pre-clinical studies and epidemiological observations in patients with an excess of GH and IGF-1 production or treated with these hormones showed a positive association with the risk to develop several types of cancer. In conclusion, athletes should be made aware that long-term treatment with doping agents might increase the risk of developing cancer, especially if associated with other licit or illicit drugs and/or high-protein diet

The DLV System for Knowledge Representation and Reasoning

This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to $\Delta^P_3$-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof. Furthermore, we illustrate the general architecture of the DLV system which has been influenced by these results. As for applications, we overview application front-ends which have been developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.Comment: 56 pages, 9 figures, 6 table

The prevention of doping and the improper use of drugs and food supplements in sports and physical activities: a survey on the activity of the prevention departments of Italian local health authorities

Doping is an important public health problem widespread not only among elite athletes, but also among amateur and recreational athletes and the general population. In Italy the introduction of doping prevention within the Essential Levels of Care (LEA) with the DPCM 12/1/2017 represents a crucial step towards the implementation of education and health promotion interventions. In this context, the Departments of Prevention (DP) of the Local Health Authorities (LHA) have to play a fundamental role, becoming the cultural and operational reference on this issue. As part of the "Doping prevention: development of a permanent educational tool coordinated by the National Health Service Prevention Departments" project, funded by the Italian Ministry of Health, a survey was conducted on the activities carried out by the DP regarding doping prevention and improper use and abuse of drugs and food supplements in sports and physical activities, as a basis for the harmonization of organizational structures and prevention programs and the creation of a collaboration network at a regional and national level

A Comparison of the Conditioning Regimens BEAM and FEAM for Autologous Hematopoietic Stem Cell Transplantation in Lymphoma: An Observational Study on 1038 Patients From Fondazione Italiana Linfomi

Abstract Background Carmustine (BCNU)-Etoposide-Citarabine-Melphalan (BEAM) chemotherapy is the standard conditioning regimen for autologous stem cell transplantation (ASCT) in lymphomas. Owing to BCNU shortages, many centers switched to Fotemustine-substituted BEAM (FEAM), lacking proof of equivalence. Methods We conducted a retrospective cohort study in 18 Italian centers to compare safety and efficacy of BEAM and FEAM regimens for ASCT in lymphomas performed from 2008 to 2015. Results We enrolled 1038 patients (BEAM n=607, FEAM n=431), of which 27% had Hodgkin's lymphoma (HL), 14% indolent Non-Hodgkin's lymphoma (iNHL) and 59% aggressive NHL (aNHL). Baseline characteristics including age, sex, stage, B-symptoms, extranodal involvement, previous treatments, response before ASCT, overall conditioning intensity, were well balanced between BEAM and FEAM; notable exceptions were: ASCT year (median: BEAM=2011 vs FEAM=2013, p Conclusions BEAM and FEAM do not appear different in terms of survival and disease control. However, due to concerns of higher toxicity, Fotemustine substitution in BEAM does not seem justified, if not for easier supply

Understanding Automatic Diagnosis and Classification Processes with Data Visualization

Providing accurate diagnosis of diseases generally requires complex analyses of many clinical, biological and pathological variables. In this context, solutions based on machine learning techniques achieved relevant results in specific disease detection and classification, and can hence provide significant clinical decision support. However, such approaches suffer from the lack of proper means for interpreting the choices made by the models, especially in case of deep-learning ones. In order to improve interpretability and explainability in the process of making qualified decisions, we designed a system that allows for a partial opening of this black box by means of proper investigations on the rationale behind the decisions; this can provide improved understandings into which pre-processing steps are crucial for better performance. We tested our approach over artificial neural networks trained for automatic medical diagnosis based on high-dimensional gene expression and clinical data. Our tool analyzed the internal processes performed by the networks during the classification tasks in order to identify the most important elements involved in the training process that influence the network's decisions.We report the results of an experimental analysis aimed at assessing the viability of the proposed approach

Towards solving path planning in keyhole neurosurgery with answer set programming

Keyhole neurosurgery is one of the most hazardous procedures, due to the complexity of the brain environment and the high density of vital structures. Complying with the kinematic constraints of the probe selected to perform the procedure and of the preferences dictated by surgeons' experience are essential to find the safest path to the surgical target. This work presents and optimisation and classification strategy for neurosurgical interventions. The framework relies on Answer Set Programming to translate the requirements and the expert's knowledge into the objectives of the optimisation procedure. The semantics of Answer Set Programming grants extensive flexibility, as it allows to easily change the requirements to optimise and their priority based on the specific needs of the clinical case