Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures, with use of trained artificial intelligence (AI) models, are disclosed herein. In an example, a use of a medical evaluation workflow involving an AI model includes: obtaining image data and non-image data associated with a medical imaging study; using at least one AI model to analyze the image data, with the trained AI model being validated with a defined governance standard to identify a characteristic or particular type of characteristic; identifying the characteristic with the AI model; and communicating the identified characteristic to a location associated with evaluation of the medical imaging study.

Abstract: A machine for forming a container from a blank of sheet material includes an outer body frame, a mandrel mounted to the body frame, a lifting assembly coupled to the body frame, and a lateral presser assembly and a folding arm assembly coupled to the lifting assembly, wherein the lifting assembly moves the lateral presser assembly and the folding arm assembly in a first direction and an opposite, second direction. A folding arm servomechanism is operatively connected to a folding arm of the folding arm assembly and rotates the folding arm relative to the mandrel to wrap a first portion of the blank around the mandrel. A lateral presser servomechanism is operatively connected to an engaging bar of the lateral presser assembly and rotates the engaging bar relative to the mandrel to wrap a second portion of the blank around the mandrel to at least partially form the container.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures, with use of trained artificial intelligence (AI) models, are disclosed herein. In an example, a use of a medical evaluation workflow involving an AI model includes: obtaining image data and non-image data associated with a medical imaging study; using at least one AI model to analyze the image data, with the trained AI model being validated with a defined governance standard to identify a characteristic or particular type of characteristic; identifying the characteristic with the AI model; and communicating the identified characteristic to a location associated with evaluation of the medical imaging study.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: A machine for forming a container from a blank of sheet material includes an outer body frame, a mandrel mounted to the body frame, a lifting assembly coupled to the body frame, and a lateral presser assembly and a folding arm assembly coupled to the lifting assembly, wherein the lifting assembly moves the lateral presser assembly and the folding arm assembly in a first direction and an opposite, second direction. A folding arm servomechanism is operatively connected to a folding arm of the folding arm assembly and rotates the folding arm relative to the mandrel to wrap a first portion of the blank around the mandrel. A lateral presser servomechanism is operatively connected to an engaging bar of the lateral presser assembly and rotates the engaging bar relative to the mandrel to wrap a second portion of the blank around the mandrel to at least partially form the container.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: A barrel formed from a sheet of blank material includes a plurality of side wall panels for forming sides of the barrel including a front panel, a rear panel, two opposing end panels, and at least one diagonal corner panel, at least one bottom flap for forming a bottom of the barrel, and a plurality of top flaps for forming a top of the barrel including a top front flap foldably connected to the front panel, a top rear flap foldably connected to the rear panel, and two opposing top end flaps each foldably connected to one of the end panels. The top front flap and the top rear flap include at least one closure slot. Each of the two opposing top end flaps includes at least one locking finger. The locking fingers are inserted within the closure slots for securing the top of the barrel in a closed position.

Abstract: Systems and methods for coordinating a medical imaging workflow with the use of preparation and coordination actions, and similar pre-processing protocols are disclosed herein. Imaging procedure data from a medical imaging study, such as image data and order data produced from imaging procedures (e.g., radiological imaging procedures) at medical facilities is processed and presented for review to a preparing user. The preparing user is offered the ability to change the display characteristics of image presentation, supplement erroneous or incomplete data and information of the study, open a support request for the study, or associate prior or comparison images with the study. The changes provided by the preparing user within these or other portions of a preparation protocol may be used to affect a subsequent display of the medical imaging study, and in some examples, to affect the assignment of the study in the workflow to particular imaging users.

Abstract: Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

Abstract: Systems and methods for coordinating a medical imaging workflow with the use of preparation and coordination actions, and similar pre-processing protocols are disclosed herein. Imaging procedure data from a medical imaging study, such as image data and order data produced from imaging procedures (e.g., radiological imaging procedures) at medical facilities is processed and presented for review to a preparing user. The preparing user is offered the ability to change the display characteristics of image presentation, supplement erroneous or incomplete data and information of the study, open a support request for the study, or associate prior or comparison images with the study. The changes provided by the preparing user within these or other portions of a preparation protocol may be used to affect a subsequent display of the medical imaging study, and in some examples, to affect the assignment of the study in the workflow to particular imaging users.

Abstract: A barrel formed from a sheet of blank material includes a plurality of side wall panels for forming sides of the barrel including a front panel, a rear panel, two opposing end panels, and at least one diagonal corner panel, at least one bottom flap for forming a bottom of the barrel, and a plurality of top flaps for forming a top of the barrel including a top front flap foldably connected to the front panel, a top rear flap foldably connected to the rear panel, and two opposing top end flaps each foldably connected to one of the end panels. The top front flap and the top rear flap include at least one closure slot. Each of the two opposing top end flaps includes at least one locking finger. The locking fingers are inserted within the closure slots for securing the top of the barrel in a closed position.

Abstract: A barrel formed from a sheet of blank material includes a plurality of side wall panels for forming sides of the barrel including a front panel, a rear panel, two opposing end panels, and at least one diagonal corner panel, at least one bottom flap for forming a bottom of the barrel, and a plurality of top flaps for forming a top of the barrel including a top front flap foldably connected to the front panel, a top rear flap foldably connected to the rear panel, and two opposing top end flaps each foldably connected to one of the end panels. The top front flap and the top rear flap include at least one closure slot. Each of the two opposing top end flaps includes at least one locking finger. The locking fingers are inserted within the closure slots for securing the top of the barrel in a closed position.

Abstract: A barrel formed from a sheet of blank material includes a plurality of side wall panels for forming sides of the barrel including a front panel, a rear panel, two opposing end panels, and at least one diagonal corner panel, at least one bottom flap for forming a bottom of the barrel, and a plurality of top flaps for forming a top of the barrel including a top front flap foldably connected to the front panel, a top rear flap foldably connected to the rear panel, and two opposing top end flaps each foldably connected to one of the end panels. The top front flap and the top rear flap include at least one closure slot. Each of the two opposing top end flaps includes at least one locking finger. The locking fingers are inserted within the closure slots for securing the top of the barrel in a closed position.

Abstract: A barrel formed from a sheet of blank material includes a plurality of side wall panels for forming sides of the barrel including a front panel, a rear panel, two opposing end panels, and at least one diagonal corner panel, at least one bottom flap for forming a bottom of the barrel, and a plurality of top flaps for forming a top of the barrel including a top front flap foldably connected to the front panel, a top rear flap foldably connected to the rear panel, and two opposing top end flaps each foldably connected to one of the end panels. The top front flap and the top rear flap include at least one closure slot. Each of the two opposing top end flaps includes at least one locking finger. The locking fingers are inserted within the closure slots for securing the top of the barrel in a closed position.

Abstract: A machine for forming a case from a blank of sheet material includes a body, a mandrel mounted on the body and having an external shape complimentary to an internal shape of at least a portion of the case, a member mounted on the body adjacent the mandrel for applying a force to the blank for at least one of folding a portion of the blank around the mandrel, moving the blank, and securing portions of the blank together, and a servomechanism operatively connected to the member for driving and controlling movement of the member to apply the force to the blank.