Abstract: The application relates to assessing cognitive impairment and/or speech motor impairment. The method comprises analysing a voice recording from a word-reading test by identifying a plurality of segments of the voice recording that correspond to single words or syllables and determining the number of correctly read words in the voice recording and/or the speech rate. Determining the correct number of words in the recording may comprise computing one or more Mel-frequency cepstral coefficients for the segments, clustering the resulting vectors of values into n clusters, wherein each cluster has n possible labels, predicting a sequence of words in the voice recording using the labels associated with the clustered vectors of values, performing a sequence alignment between the predicted sequence of words and the sequence of words used in the word reading test, selecting the labels that result in the best alignment and counting the number of matches in the alignment.

Abstract: The present application relates to antibodies which bind specifically to chelated radionuclides, including bispecific antibodies. It further relates to the use of such bispecific antibodies in applications such as radioimmunoimaging and radioimmunotherapy. It additionally relates to clearing agents and compositions useful in such methods.

Abstract: Currently, assessing the severity and progression of symptoms in a subject diagnosed with a muscular disability, in particular SMA involves in-clinic monitoring and testing of the subject every 6 to 12 months. However, monitoring and testing a subject more frequently is preferred, but increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the subject. Thus, assessing the severity and progression of symptoms via remote monitoring and testing of the subject outside of a clinic environment as described herein provides advantages in cost, ease of monitoring and convenience to the subject. Systems, methods and devices according to the present disclosure provide a diagnostic for assessing of the axial motor function of a subject having a muscular disability, in particular SMA by active testing of the subject.

Abstract: Aspects described herein relate to the field of disease tracking and diagnostics. Specifically, they relate to a method of assessing a muscular disability and, in particular, spinal muscular atrophy (SMA) in a subject comprising the steps of determining at least one parameter from a dataset of sensor measurements of the subject using a mobile device, and comparing the determined at least one parameter to a reference, whereby the muscular disability and, in particular, SMA will be assessed. Aspects described herein also relate to a mobile device comprising a processor, at least one pressure sensor and a database as well as software which is tangibly embedded to said device and, when running on said device, carries out the method of the invention as well as the use of such a device for assessing a muscular disability and, in particular, SMA.

Abstract: Currently, assessing the severity and progression of symptoms in a subject diagnosed with a muscular disability, in particular SMA involves in-clinic monitoring and testing of the subject every 6 to 12 months. However, monitoring and testing a subject more frequently is preferred, but increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the subject. Thus, assessing the severity and progression of symptoms via remote monitoring and testing of the subject outside of a clinic environment as described herein provides advantages in cost, ease of monitoring and convenience to the subject. Systems, methods and devices according to the present disclosure provide a diagnostic for assessing of the axial motor function of a subject having a muscular disability, in particular SMA by active testing of the subject.

Abstract: A computer implemented method for classifying quality of biological sensor data (110) comprising the following steps: providing biological sensor data (110) obtained by at least one biological sensor (112), wherein the biological sensor data (110) comprises at least one signal (116); classifying quality of the signal (116) by using at least one trained trainable model (119), wherein the trainable model (119) is trained on historical biological sensor data based on a supervised (134) and/or semi-supervised deep learning architecture (188), wherein the trainable model (119) is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification. Further, a biological sensor (112), a computer program and a computer-readable storage medium are disclosed.

Abstract: Methods and systems for determining a location of an individual in a home, business, structure, or other finite amount of space using one or more beacons.

Abstract: The application relates to devices and methods for assessing cognitive impairment and/or speech motor impairment in a subject. The method comprises analysing a voice recording from a word- reading test obtained from the subject by identifying a plurality of segments of the voice recording that correspond to single words or syllables and determining the number of correctly read words in the voice recording and/or the speech rate associated with the recording.

Abstract: A computer implemented method for determining accuracy of heart rate variability is proposed. The method comprises the following steps: a) providing at least one photoplethysmogram obtained by at least one portable photoplethysmogram device (110); b) Determining at least one signal feature by evaluating the photoplethysmogram; c) Determining the accuracy of heart rate variability by using at least one trained model, wherein the signal features determined in step b) are used as input for the trained model.

Abstract: The present invention relates to the field of diagnostics. Specifically, it relates to a method for assessing Huntington's disease of the pre-manifest stage in a subject comprising the steps of determining at least one performance parameter from a dataset of fine motoric measurements from said subject, comparing the determined at least one performance parameter to a reference, and assessing Huntington's disease of the pre-manifest stage in the subject based on said comparison. Yet, the invention contemplates a device and a system for carrying out the aforementioned methods and the use of such device or system for assessing Huntington's disease of the pre-manifest stage in the subject.

Abstract: The present application relates to antibodies which bind specifically to chelated radionuclides, including bispecific antibodies. It further relates to the use of such bispecific antibodies in applications such as radioimmunoimaging and radioimmunotherapy. It additionally relates to clearing agents and compositions useful in such methods. The chelated radionuclides may be a DOTAM-lead (Pb) chelate.

Abstract: Medical device systems and methods are disclosed herein for assessing motor symptoms in Parkinson's disease patients through passively and/or actively acquired sensor data of motor behaviors and generating motor system assessments. Passive movement data and/or active motor performance data collected from Parkinson's disease patients is representative of EQ-5D-5L, PDQ-39, and/or MDS-UPDRS health status measurement data to more accurately predict quality of life, health status, and disease severity of Parkinson's disease patients. The generated motor system assessment may be used to provide neurologists and other health professionals with more accurate treatment recommendations and disease management protocols rather than using patient surveys alone.

Abstract: The present invention relates to the field of disease tracking and potentially even diagnostics. Specifically, it relates to a method for predicting the total motor score (TMS) in a subject suffering from Huntington's Disease (HD) comprising the steps of determining at least one performance parameter from a dataset of measurements of central motor function capabilities from said subject, comparing the determined at least one performance parameter to a reference obtained from a computer-implemented regression model generated on training data, in an embodiment using partial least-squares (PLS) analysis, with the at least one performance parameters, and predicting the TMS of the subject based on said comparison.

Abstract: A machine learning system (110) for determining at least one analysis model for predicting at least one target variable indicative of a disease status is proposed.

Abstract: Aspects of the disclosure relate to mobile-optimized software tools that may be deployed and used to measure spatial working memory across diverse clinical trial populations. For example, some aspects describe computational optimization and participatory design of a novel spatial working memory task for clinical trials relating to autism spectrum disorders (ASD) or other neurological conditions (e.g., Alzheimer's disease). Software tools as described herein may be used for measuring treatment effects on spatial working memory and/or provide treatments to improve a patient's spatial working memory. Digital biomarkers may be generated for each patient based on the patient's spatial working memory.

Abstract: The present invention relates to antibodies which bind to antigens on target cells and which target radionuclides to said cells, and to methods of using the same.

Abstract: The present invention relates to the field of disease tracking. Specifically, it relates to a method for predicting the total motor score (EDSS) in a subject suffering from multiple sclerosis (MS) comprising the steps of determining at least one performance parameter from a dataset of measurements of active and passive gait and posture capabilities and cognitive capabilities from said subject, comparing the determined at least one performance parameter to a reference obtained from a computer-implemented regression model generated on training data using random forest (RF) analysis, and predicting the EDSS of the subject based on said comparison. The present invention also relates to a mobile device and/or a remote device as well as software which is tangibly embedded to one of the devices and carries out the method of the invention, wherein said mobile device and said remote device can be operatively linked to each other.

Abstract: The present invention relates to the field of disease tracking. Specifically, it relates to a method for predicting the forced vital capacity (FVC) in a subject suffering from spinal muscular atrophy (SMA) comprising the steps of determining at least one performance parameter from a dataset of measurements of central motor function capabilities from said subject, comparing the determined at least one performance parameter to a reference obtained from a computer-implemented regression model generated on training data using partial least-squares (PLS) analysis with the at least one performance parameters, and predicting the FVC of the subject based on said comparison. The present invention also relates to a mobile device and/or a remote device as well as software which is tangibly embedded to one of the devices and which carries out the method of the invention, wherein said mobile device and said remote device are operatively linked to each other.

Abstract: Currently, assessing the severity and progression of symptoms in a subject diagnosed with a muscular disability, in particular SMA involves in-clinic monitoring and testing of the subject every 6 to 12 months. However, monitoring and testing a subject more frequently is preferred, but increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the subject. Thus, assessing the severity and progression of symptoms via remote monitoring and testing of the subject outside of a clinic environment as described herein provides advantages in cost, ease of monitoring and convenience to the subject. Systems, methods and devices according to the present disclosure provide a diagnostic for assessing of the distal motor function of a subject having a muscular disability, in particular SMA by active testing of the subject.

Abstract: Currently, assessing the severity and progression of symptoms in a subject diagnosed with a muscular disability, in particular SMA involves in-clinic monitoring and testing of the subject every 6 to 12 months. However, monitoring and testing a subject more frequently is preferred, but increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the subject. Thus, assessing the severity and progression of symptoms via remote monitoring and testing of the subject outside of a clinic environment as described herein provides advantages in cost, ease of monitoring and convenience to the subject. Systems, methods and devices according to the present disclosure provide a diagnostic for assessing of the motor function of a subject having a muscular disability, in particular SMA by active testing of the subject.