Ms-technologies Gmbh

Deus Technologies LLC

Yuan-Ming Fleming Lure

Jyh-Shyan Lin

Hwa-Young Michael Yeh

Xin-Wei Xu

Shuchuan Jack Cheng

Ruiping Li

Teresa Wu

William Bredlow

Jing Li

Kewei Chen

Rong Feng Zhuang

Yi Su

Xiaonan Liu

David Weidman

Richard Lipscher

Matthew Thomas Freedman

A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.

System and method for patient movement detection and fall monitoring

A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

System and method for Alzheimer's disease risk quantification utilizing interferometric micro-doppler radar and artificial intelligence

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

System and method for diagnostics and prognostics of mild cognitive impairment using deep learning

A system for passively predicting and detecting falls using one or more dual-polarized Doppler radars and machine learning algorithms. The system is typically implemented for use in predicting or detecting falls in older adults and may be connected with various systems that can alert emergency services or hospice personnel in the event of a fallen individual. Furthermore, the system overcomes conventional radar systems by integrating vertical and horizontal micro-Doppler signatures into a combined signature which is analyzed by machine learning algorithms to correctly and expeditiously predict and detect a variety of human movements. The system also finds applications wherever micro-Doppler signals may be generated such as predicting or detecting behaviors or movements over time to detect and predict the onset of diseases and other disabilities.

Doppler radar system with machine learning applications for fall prediction and detection

A method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.

Method and system for the detection of lung nodule in radiological images using digital image processing and artificial neural network

A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.

Fuzzy logic based classification (FLBC) method for automated identification of nodules in radiological images

A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.

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Potomac, MD, United States of America

Yuan-Ming Fleming Lure