Liver cancer is estimated to be the fifth most common in the world, while it is also considered the third leading cause of cancer death. In cases of primary liver cancer, surgery in combination with chemotherapy and radiotherapy can lead to a complete cure or significantly increase the patient’s life expectancy. Since the liver is an organ that performs several critical functions in the human body, the precise estimation of the disease (position and size of tumors and its vicinity to vessels) plays a vital role in a successful operation. In some cases, the removal of the tumor may be successful, but the percentage of the hepatic remnant may not be sufficient to sustain life. Therefore, accurate imaging of the tumor of the liver and proper planning of a difficult surgery to remove tumor(s) from a patient’s liver can be a lifesaver and lead to a complete cure of the disease. The aim of the present study is the initial accurate representation of the liver (parenchyma, tumors, vessels) as a digital three-dimensional (3D) model using advanced image processing and machine learning techniques and its 3D printing in 1:1 scale representing the full size of the liver with the tumor(s). A model of this type has been used at our University surgical department to plan complex hepatobiliary surgeries, provide more accurate information to the patients and their families, as well as improve the training of medical students and resident surgeons and fellows.

Introduction: 3-dimensional (3D) printing and augmented reality (AR) are emerging technologies that are used in a wide variety of scientific fields. Among them, medicine is one of the most promising fields of application since these technologies can benefit not only surgeons, but also medical/surgical trainees, patients and can potentially benefit health care systems with better educated staff working on personalized solutions for the patients. Thus, potentially reducing intra-operative and post operative complications and overall costs for the health care systems. Hepatic malignancy surgeries are some of the most demanding surgeries that could a general surgeon perform. The intra-operative and post-operative risks and complications render them demanding. In literature there are cases of research studies including applications of 3D printing and augmented reality in hepatic malignancies. Methods: For this, a comprehensive literature search was conducted on Scopus and Pubmed databases (latest search September 5, 2024). Research studies that included applications of 3D printing and AR in hepatic malignancies were eligible for the review. Results: Herein, twelve papers have been included and presented, which either include the use of 3D printing or the use of AR. There are some cases where both technologies were used simultaneously. 3D printing technology and AR can be used alone or in combination together to aid in the management of hepatic malignancies. Conclusion: Encouraging results (eg, efforts to reduce cost of 3D printing, proper surgical pre-planning, usefulness in education of medical personnel and patients) from the use of these technologies, not only qualitatively but also quantitatively, show that the medical staff can help patients and improve their part of the health system. Yet much more studies need to validate whether the use of these two technologies provides positive results on the surgeries or not.

Water quality monitoring is essential for assessing a freshwater ecosystem’s status. This knowledge is indispensable for selecting restoration measures to ensure the provision of ecosystem services and sustainable growth of human communities. Remote sensing (RS) has proven to be effective for this purpose, offering broad coverage and high temporal and spatial resolution, which is particularly important for small water bodies. In this study, UAV-based multispectral imagery is employed to estimate key water quality parameters, namely, Chlorophyll-a (Chl-a) and turbidity, which are relevant to global and national legislation and policies. Machine learning models were developed using the support vector regression (SVR) algorithm. The Chl-a model resulted in an R² value of 0.49 and an RMSE of 0.24 μg/L, while the turbidity model resulted in an R² value of 0.70 and an RMSE of 0.38 Formazin Nephelometric Unit (FNU). These models enabled the generation of detailed spatial distribution maps of water quality indicators for the studied river. The proposed approach provides valuable information that supports monitoring for both pressure and restoration impacts, promoting the sustainability of freshwater ecosystems.

This paper presents FireVision, an innovative platform and model for real-time fire detection and monitoring. The platform utilizes automated drone flights to collect high-resolution imagery in both suburban and forested settings. Ensemble deep learning inference, based on Mask R-CNN weak learners, is employed to trigger alerts. Detection performance is further enhanced by integrating ResNet-50, ResNet-101, and ResNet-152 classifiers, which can be deployed in the cloud or on the drone’s edge co-processing units. Additionally, a fire criticality index is introduced, leveraging detection bounds and masks to assess the severity of fire events, alongside an automated drone path-planning algorithm for identifying critical fire incidents. Experiments were conducted using a supervised, mask-annotated dataset to evaluate model accuracy and inference speed across various cloud and edge computing configurations. Results indicate that ResNet-101 surpasses ResNet-50 by 5 to 12.5 percent in mAP@0.5 mask accuracy, with an 18 percent increase in inference time on the cloud and a 27 percent increase on the drone edge device GPU. In comparison, ResNet-152 achieves a 0.5 to 1.2 percent improvement in mAP@0.5 over ResNet-101, but its inference time is nine times slower in the cloud and 1.3 times slower on the GPU.

Smart Eye is a research project focused on developing and operating, in real-time, an innovative system, that allows the visualization of invisible, known, covered by the ground or by constructions, monuments and finds in an archaeological site, using an Augmented Reality (AR) environment. The visitors of an archaeological site will be able to observe the covered antiquities (3D models and descriptive information) on the screen of their mobile device (smart phone and/or tablet). The system integrates mobile positioning and pose estimation technologies, AR algorithms, Geographic Information Systems (GIS) and spatial databases. The AR application is implemented by IT scientists and is subsequently evaluated in the field to identify both technical problems (e.g. are files uploaded at high speed?) and problems identified by the average user of the AR application (e.g. would it be better if the button to display the 3D model of the archaeological excavation was larger on the screen or of a different color?). The paper will present, on one hand, the validation and evaluation protocols according to the relevant literature, and on the other, the exact evaluation methodology of the Smart Eye system. In addition, the problems identified and the way they were solved during the 1st evaluation of the application will be presented, as well as the problems identified during the 2nd evaluation.