Medical robots with potential applications in participatory and opportunistic remote sensing: A review

Among numerous applications of medical robotics, this paper concentrates on the design, optimal use and maintenance of the related technologies in the context of healthcare, rehabilitation and assistive robotics, and provides a comprehensive review of the latest advancements in the foregoing field of science and technology, while extensively dealing with the possible applications of participatory and opportunistic mobile sensing in the aforementioned domains. The main motivation for the latter choice is the variety of such applications in the settings having partial contributions to functionalities such as artery, radiosurgery, neurosurgery and vascular intervention. From a broad perspective, the aforementioned applications can be realized via various strategies and devices benefiting from detachable drives, intelligent robots, human-centric sensing and computing, miniature and micro-robots. Throughout the paper tens of subjects, including sensor-fusion, kinematic, dynamic and 3D tissue models are discussed based on the existing literature on the state-of-the-art technologies. In addition, from a managerial perspective, topics such as safety monitoring, security, privacy and evolutionary optimization of the operational efficiency are reviewed

Lokaalse faasi kvantimise tunnusjoonte eraldamisel põhinev vanuse ja soo hindamine kasutades konvulutsionaalset närvivõrku

Even though artificial neural networks are one of the oldest machine learning techniques, there were no many experiments on them by 2010s because of its computational complexity. Artificial neural networks got inspired by human neural anatomy, and try to achieve similar accuracy. Latest advances of silicon technology enable us to conduct experiments on all types of artificial neural networks. Convolutional Neural Networks are one of state-of-art neural network types. As a human, we all have great recognition, detection mechanism in our body. In this study, it will be attempted to gain similar ability with computer aid of CNNs. As all other supervisedlearning methods, we need training and testing dataset. We are going to apply CNN on apparent age and gender estimation. There are few public dataset which are created for age estimation. One of them and the biggest one is IMDB-Wiki dataset which contains pictures of famous people from wikipedia and IMDB with their real-age label. In order to create real-age label, the creator used the time differences between photo-taken year and birth year. However for better accuracy, we need apparent age information. Because aging is a process that depends on many conditions. As it is going to be explained later, we collected Japanese dataset on the internet, and labeled their apparent ages by weighted voting. After collecting the image data sets, we pre-processed the images with face detection and alignment methods. Afterwards, we copied all images and used Local Phase Quantization(LPQ) method to extract their features. In CNN, it is always better to use pre-trained data and fine-tune it. Thus we used deep face recognition pre-trained data with almost 2 millions images. After that, we fine tuned images(with LPQ and without LPQ separately) with using the label distribution encoding. Finally we had 2 CNN data. For combining the results, we took the mean of all respective output neurons. At the end, expected values of all neurons are considered as apparent age information. For gender classification, we just trained the system in the similar way. Only difference is that we have only 2 output neurons for gender classification, besides LPQ is not applied in gender classification

From apparent to real age: Gender, age, ethnic, makeup, and expression bias analysis in real age estimation

Real age estimation in still images of faces is an active area of research in the computer vision community. However, very few works attempted to analyse the apparent age as perceived by observers. Apparent age estimation is a subjective task, which is affected by many factors present in the image as well as by observer's characteristics. In this work, we enhance the APPA-REAL dataset, containing around 8K images with real and apparent ages, with new annotated attributes, namely gender, ethnic, makeup, and expression. Age and gender from a subset of guessers is also provided. We show there exists some consistent bias for a subset of these attributes when relating apparent to real age. In addition we run simple experiments with a basic Convolutional Neural Network (CNN) showing that considering apparent labels for training improves real age estimation rather than training with real ages. We also perform bias correction on CNN predictions, showing that it further enhance final age recognition performance

Prognostic factors for survival in metastatic renal cell carcinoma patients with brain metastases receiving targeted therapy

Background: The primary objective of our study was to examine the clinical outcomes and prognosis of patients with metastatic renal cell carcinoma (mRCC) with brain metastases (BMs) receiving targeted therapy. Patients and methods: Fifty-eight patients from 16 oncology centers for whom complete clinical data were available were retrospectively reviewed. Results: The median age was 57 years (range 30-80). Most patients underwent a nephrectomy (n = 41; 70.7%), were male (n = 42; 72.4%) and had clear-cell (CC) RCC (n = 51; 87.9%). Patients were treated with first-line suni-tinib (n = 45; 77.6%) or pazopanib (n = 13; 22.4%). The median time from the initial RCC diagnosis to the diagnosis of BMs was 9 months. The median time from the first occurrence of metastasis to the development of BMs was 7 months. The median overall survival (OS) of mRCC patients with BMs was 13 months. Time from the initial diagnosis of systemic metastasis to the development of BMs (2; p2) were independent risk factors for a poor prognosis

Prognostic factors for survival in metastatic renal cell carcinoma patients with brain metastases receiving targeted therapy

WOS: 000454667200024PubMed ID: 28731496Background: The primary objective of our study was to examine the clinical outcomes and prognosis of patients with metastatic renal cell carcinoma (mRCC) with brain metastases (BMs) receiving targeted therapy. Patients and methods: Fifty-eight patients from 16 oncology centers for whom complete clinical data were available were retrospectively reviewed. Results: The median age was 57 years (range 30-80). Most patients underwent a nephrectomy (n = 41; 70.7%), were male (n = 42; 72.4%) and had clear-cell (CC) RCC (n = 51; 87.9%). Patients were treated with first-line suni-tinib (n = 45; 77.6%) or pazopanib (n = 13; 22.4%). The median time from the initial RCC diagnosis to the diagnosis of BMs was 9 months. The median time from the first occurrence of metastasis to the development of BMs was 7 months. The median overall survival (OS) of mRCC patients with BMs was 13 months. Time from the initial diagnosis of systemic metastasis to the development of BMs (2; p2) were independent risk factors for a poor prognosis

Is Change in Hemoglobin Level a Predictive Biomarker of Tyrosine Kinase Efficacy in Metastatic Renal Cell Carcinoma? A Turkish Oncology Group Study

Background: There are insufficient predictive markers for renal cell carcinoma (RCC). Methods: A total of 308 metastatic RCC patients were analyzed retrospectively. Results: The increased hemoglobin (Hb) group had significantly higher progression-free survival and overall survival (OS) compared with the decreased Hb group at 11.5 versus 6.35months (p < .001) and 21.0 versus 11.36months (p < .001) respectively. The 1- and 3-year OS rates were higher in the Hb increased group, i.e., 84% versus 64% and 52% versus 35% respectively. Conclusions: The present study showed that increased Hb levels after tyrosine kinase inhibitor therapy could be a predictive marker of RCC