Abstract: CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.

Abstract: Systems and methods for estimating optimal gantry orientation and/or scanning parameters using image(s) from one or several cameras for a CT scan of a patient. Images are acquired of a patient. An optimal gantry tilt is estimated that keeps the patient's eye lenses outside a range of a beam during the scan. The CT scan is performed using the optimal gantry tilt. A scout scan is not used.

Abstract: A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Abstract: A thermal interface material, an integrated circuit formed therewith, and a method of application thereof are provided. The thermal interface material includes 5% to 30% by volume of a polymer component and at least 70% by volume of liquid metal droplets, all based on total volume of the thermal interface material. The polymer component has a first polymer having a molecular weight in a range of 400 g/mol to 400,000 g/mol. The liquid metal droplets are dispersed throughout the polymer component.

Abstract: A die of a circuit assembly and an upper layer of a circuit assembly are thermally connected by applying a thermal interface material (TIM) on the die, such that the TIM is between the die and an upper layer. The TIM comprises an emulsion of liquid metal droplets, rigid particles, and uncured polymer. The method further comprises compressing the circuit assembly thereby deforming the liquid metal droplets and forming a bondline distance between the die and upper layer that 90% to 110% of the average particle size of the rigid particles. An average particle size of the liquid metal droplets in the thermal interface material prior to applying is greater than the average particles size of the rigid particles. The thermal interface material is cured thereby forming the circuit assembly.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides a segmentation determination and/or an HILN (hemolysis, icterus, lipemia, normal) determination of the specimen while providing characterization training updates based on the accuracy and/or confidence in the determinations. The method includes identifying an incorrect or low confidence segmentation or HILN determination, forwarding the incorrect or low confidence determination from the HILN network to a database, and providing one or more training images to the HILN network based on the incorrect or low confidence determination. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides an HILN classification (hemolysis, icterus, lipemia, normal) of the specimen along with a basis for that determination. The method includes assigning a hash code to each training image of a sample specimen used in the characterization training process. In response to an HILN determination for a test specimen, the method can retrieve via the hash code one or more of the closest matching training images upon which the HILN classification is based. The one or more of the closest matching training images can be displayed alongside of the one or more images of the test specimen. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen and specimen container to be analyzed in an automated diagnostic analysis system. The method can provide a segmentation determination and/or an HILN determination (hemolysis, icterus, lipemia, or normal) of the specimen while protecting patient information. The method includes capturing an image of a specimen container via an image capture device, identifying a label affixed to the specimen container in the captured image via an anonymization network, and editing the captured image via the anonymization network to mask some or all information present in the label so that it is removed from the captured image. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A system and method for sharing communication platform data includes (a) displaying on a screen of a mobile device a first and second data field, wherein the first data field includes a QR code and the second data field includes a plurality of visual indicia corresponding to contact information corresponding to a user of the mobile device; (b) visually distinguishing on the screen at least one selected visual indicia from at least one unselected visual indicia upon receiving an input corresponding to a selection of the at least one visual indicia by the user; and (c) updating in real-time the QR code to include the contact information corresponding to the selected visual indicia and, optionally, wherein the updated QR code corresponds to a URL containing the contact information.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: Systems and methods for automatic assessment of a vessel are provided. A temporal sequence of medical images of a vessel of a patient is received. A plurality of sets of output embeddings is generated using a machine learning based model trained using multi-task learning. The plurality of sets of output embeddings is generated based on shared features extracted from the temporal sequence of medical images. A plurality of vessel assessment tasks is performed by modelling each of the plurality of sets of output embeddings in a respective probabilistic distribution. Results of the plurality of vessel assessment tasks are output.

Abstract: An integrated circuit (IC), including a functional circuit and a security system, is disclosed. The functional circuit generates a request packet for an indirect memory access of a memory. The security system validates the functional circuit based on a security attribute and a functional identifier of the functional circuit. Based on the request packet and the validation of the functional circuit, the security system identifies an instruction sequence associated with the indirect memory access. Further, the security system determines a type of the indirect memory access based on the instruction sequence, and validates the type of the indirect memory access based on the security attribute and the request packet. Based on the validation of the type of the indirect memory access, the instruction sequence is executed, thereby facilitating the indirect memory access for the functional circuit.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images of a patient are received. The one or more input medical images are encoded into embeddings using a machine learning based encoder network. A medical imaging analysis task is performed based on the embeddings. Results of the medical imaging analysis task are output.

Abstract: A pedestal assembly for a laundry appliance configured to operably connect to a water supply and drain of the laundry appliance arranged above the pedestal, the pedestal may include a cabinet defining a cavity configured to receive items to be laundered; and a shoe holder having at least one shaft configured to maintain at least one shoe thereon, wherein the shoe holder is configured to rotate within the cavity during a pedestal wash cycle and the shoe holder is configured to maintain a fixed position within the cavity for storage when the pedestal is not operating in a wash cycle.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

Abstract: Systems and methods for processing a C4 and C5 stream are disclosed. A pygas stream can be separated in a depentanizer to produce a C4 and C5 stream and a C6 to C9+ stream. The C4 and C5 stream is further processed to recover C5 dienes including isoprene, pentadiene, cyclopentadiene, or combinations thereof. The C6 to C9+ stream is further processed to recover aromatics including benzene, toluene, xylene, ethylbenzene, or combinations thereof.