Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

Industrial robots play a crucial role in a wide range of industrial processes, and due to the complexity of the work environment in which these systems are deployed, more robust and accurate control methods are required. Deep reinforcement learning (DRL) emerges as a comprehensive approach that does not require an initial source of structured data for its learning process. Instead, DRL generates its own data based on its experiences within a work environment. To generate its own data, DRL requires integration with virtualized environments, provided by simulators. These tools must include scenarios in industrial contexts and allow integration with ML tools, among other capabilities. Currently, there are several platforms that support the simulation of various scenarios and the generation of synthetic data, facilitating the development of end-to-end systems based on artificial intelligence, such as DRL. This article presents an extensive review of software tools applied in DRL-based control systems for robotic manipulators. The selection of these tools is based on their efficiency, scalability, and compatibility with contemporary industrial standards, offering insights into their practical application in real-world scenarios. Consequently, this paper establishes a complete framework for designing and developing control systems for robotic manipulators using end-to-end DRL. This framework outlines in detail the tools, including simulators, APIs, libraries, and methods, and their interactions with each other. Additionally, it discusses the practical implications of this framework, highlighting its potential applications in industry and addressing some of the challenges and limitations encountered in applying DRL to complex robotic systems.

Background: Colposcopy imaging is widely used to diagnose, treat and follow-up on premalignant and malignant lesions in the vulva, vagina, and cervix. Thus, deep learning algorithms are being used widely in cervical cancer diagnosis tools. In this study, we developed and preliminarily validated a model based on the Unet network plus SVM to classify cervical lesions on colposcopy images. Methodology: Two sets of images were used: the Intel & Mobile ODT Cervical Cancer Screening public dataset, and a private dataset from a public hospital in Ecuador during a routine colposcopy, after the application of acetic acid and lugol. For the latter, the corresponding clinical information was collected, specifically cytology on the PAP smear and the screening of human papillomavirus testing, prior to colposcopy. The lesions of the cervix or regions of interest were segmented and classified by the Unet and the SVM model, respectively. Results: The CAD system was evaluated for the ability to predict the risk of cervical cancer. The lesion segmentation metric results indicate a DICE of 50%, a precision of 65%, and an accuracy of 80%. The classification results’ sensitivity, specificity, and accuracy were 70%, 48.8%, and 58%, respectively. Randomly, 20 images were selected and sent to 13 expert colposcopists for a statistical comparison between visual evaluation experts and the CAD tool (p-value of 0.597). Conclusion: The CAD system needs to improve but could be acceptable in an environment where women have limited access to clinicians for the diagnosis, follow-up, and treatment of cervical cancer; better performance is possible through the exploration of other deep learning methods with larger datasets.

This work presents a quantum chemical study of Zn and Sb doping and co-doping in SnO₂ carried out by a DFT+U method. The analysis has been developed by introducing three different modifications in the otherwise pure SnO₂ system. In the first place, an oxygen vacancy was introduced within the crystal. Following, such a system was doped (separately) by Zn or Sb impurities. Finally, the best energetic positions for both Zn and Sb atoms were simultaneously introduced within the lattice. Results of the simulations show that the confined charge that appeared due to the introduction of the oxygen vacancy interacts with the dopants atoms, being this interaction mostly responsible of the observed effects, i.e., E_G shrinkage, F-centers formations, and magnetic momentum rise.