We present the use of a deep Unet convolutional neural network as an automated way of sizing nasal Positive Airway Pressure (PAP) masks using facial images of patients. Using a VGG16 backbone the network was trained with the MUCT dataset and a significant amount of data augmentation. The trained model was then applied to a small custom dataset of PAP and non-PAP patients to predict the nose widths and corresponding PAP mask sizes of each subject. The Unet model produced a mask sizing accuracy of 63.73% (116/183) and a within one size accuracy of 88.5% (162/183).This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance-We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.Patients with long conductive implants such as deep brain stimulation (DBS) leads are often denied access to magnetic resonance imaging (MRI) exams due to safety concerns associated with radiofrequency (RF) heating of implants. Experimental temperature measurements in tissue-mimicking gel phantoms under MRI RF exposure conditions are common practices to predict in-vivo heating in the tissue surrounding wire implants. Such experiments are both expensive-as they require access to MRI units-and time-consuming due to complex implant setups. Recently, full-wave numerical simulations, which include realistic MRI RF coil models and human phantoms, are suggested as an alternative to experiments. There is however, little literature available on the accuracy of such numerical models against direct thermal measurements. This study aimed to evaluate the agreement between simulations and measurements of temperature rise at the tips of wire implants exposed to RF exposure at 64 MHz (1.5 T) for different implant trajectories typically encountered in patients with DBS leads. Heating was assessed in seven patient-derived lead configurations using both simulations and RF heating measurements during imaging of an anthropomorphic head phantom with implanted wires. We found substantial variation in RF heating as a function of lead trajectory; there was a 9.5-fold and 9-fold increase in temperature rise from ID1 to ID7 during simulations and experimental measurements, respectively. There was a strong correlation (r2 = 0.74) between simulated and measured temperatures for different lead trajectories. The maximum difference between simulated and measured temperature was 0.26 °C with simulations overestimating the temperature rise.Electroencephalography (EEG) is a valuable clinical tool for grading injury caused by lack of blood and oxygen to the brain during birth. This study presents a novel end-to-end architecture, using a deep convolutional neural network, that learns hierarchical representations within raw EEG data. The system classifies 4 grades of hypoxic-ischemic encephalopathy and is evaluated on a multi-channel EEG dataset of 63 hours from 54 newborns. The proposed method achieves a testing accuracy of 79.6% with one-step voting and 81.5% with two-step voting.  https://www.selleckchem.com/products/tasquinimod.html  These results show how a feature-free approach can be used to classify different grades of injury in newborn EEG with comparable accuracy to existing feature-based systems. Automated grading of newborn background EEG could help with the early identification of those infants in need of interventional therapies such as hypothermia.Children with severe neurological disabilities may be unable to communicate or interact with their environments, depriving them of their right to play. Brain-computer interfaces (BCI) offer a means for such children to control external devices using only their brain signals, thereby introducing new opportunities for interaction. We organized the first North American BCI Game Jam to incite the development of BCI-compatible games for children. Nine games were submitted by 30 participants across North America. Games were judged by researchers and disabled children currently using BCI. Preliminary results demonstrate variety in game criteria preferences amongst the children who judged the games. The BCI Game Jam demonstrated promising potential for the creation of enjoyable games to suit the individual needs and preferences of children with severe neurological disabilities.Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.Cannulation is a routine yet challenging medical procedure resulting in a direct impact on patient outcomes. While current training programs provide guidelines to learn this complex procedure, the lack of objective and quantitative feedback impedes learning this skill more effectively. In this paper, we present a simulator for performing hemodialysis cannulation that captures the process using multiple sensing modalities that provide a multi-faceted assessment of cannulation. Further, we describe an algorithm towards segmenting the cannulation process using specific events in the sensor data for detailed analysis. Results from three participants with varying levels of clinical cannulation expertise are presented along with a metric that successfully differentiates the three participants. This work could lead to sensor-based cannulation skill assessment and training in the future potentially resulting in improved patient outcomes.