Systems and methods for preventing errors in medical imaging

Abstract

A method for preventing wrong-patient errors includes receiving a selection of a current imaging subject. The current imaging subject is selected for a current image acquisition session comprising capturing one or more current images of the current imaging subject utilizing at least a first image sensor system of a first imaging modality. The method includes accessing one or more previous images of a previous imaging subject. The one or more previous images depict the previous imaging subject according to at least a second imaging modality that is different from the first imaging modality. The method includes presenting the one or more previous images on a display system and, in response to determining that the previous imaging subject matches the current imaging subject based upon the one or more previous images, performing the current image acquisition session.

1 .- 20 . (canceled) 
     
     
         21 . A computer-implemented method for preventing wrong structure imaging errors, comprising:
 prior to performing a current image acquisition session of a structure using a first imaging modality, capturing one or more images of the structure using a second imaging modality that is different from the first imaging modality;   accessing an indication of an intended bodily structure for the current image acquisition session;   determining an imaged bodily structure by utilizing the one or more images of the structure of the second imaging modality as input to one or more artificial intelligence modules configured to determine one or more structure attributes based upon input imagery, the imaged bodily structure comprising output of the one or more artificial intelligence modules, the imaged bodily structure comprising an indication of a bodily structure represented in the one or more images of the structure of the second imaging modality; and   in response to determining one or more discrepancies between the imaged bodily structure and the intended bodily structure, preventing performance of the current image acquisition session.   
     
     
         22 . The computer-implemented method of  claim 21 , further comprising, in response to determining the one or more discrepancies between the imaged bodily structure and the intended bodily structure, presenting a notification on a user interface. 
     
     
         23 . The computer-implemented method of  claim 22 , wherein the notification comprises an audible alert. 
     
     
         24 . The computer-implemented method of  claim 22 , wherein the notification comprises a visual alert. 
     
     
         25 . The computer-implemented method of  claim 21 , wherein the one or more artificial intelligence modules comprise a convolutional neural network. 
     
     
         26 . The computer-implemented method of  claim 21 , wherein performance of the current image acquisition session using the first imaging modality is conditioned on detecting no discrepancies between the imaged bodily structure and the intended bodily structure. 
     
     
         27 . The computer-implemented method of  claim 21 , wherein the second imaging modality comprises a visible light imaging modality. 
     
     
         28 . The computer-implemented method of  claim 21 , wherein the first imaging modality comprises a medical imaging modality. 
     
     
         29 . The computer-implemented method of  claim 21 , wherein the one or more artificial intelligence modules are trained utilizing training data comprising images of bodily structures and labels or tags indicating structure type. 
     
     
         30 . The computer-implemented method of  claim 21 , further comprising:
 presenting, on a user interface, the imaged bodily structure and/or at least some of the one or more images of the structure of the second imaging modality.   
     
     
         31 . The computer-implemented method of  claim 30 , further comprising:
 receiving user input indicating whether the imaged bodily structure is erroneous.   
     
     
         32 . The computer-implemented method of  claim 31 , further comprising:
 using the user input to further train the one or more artificial intelligence modules.   
     
     
         33 . A computer-implemented method for preventing imaging errors, comprising:
 prior to performing a current image acquisition session of a structure using a first imaging modality, capturing one or more images of the structure using a second imaging modality that is different from the first imaging modality;   accessing an indication of one or more intended imaging attributes, the one or more intended imaging attributes being selected to be implemented for the current image acquisition session;   determining one or more imaging attributes by utilizing the one or more images of the structure of the second imaging modality as input to one or more artificial intelligence modules configured to determine imaging attributes based upon input imagery, the one or more imaging attributes comprising output of the one or more artificial intelligence modules; and   in response to determining one or more discrepancies between the one or more imaging attributes and the one or more intended imaging attributes (i) preventing performance of the current image acquisition session and/or (ii) presenting a notification on a user interface.   
     
     
         34 . The computer-implemented method of  claim 33 , wherein the one or more intended imaging attributes comprise an intended laterality, and wherein the one or more imaging attributes comprise an imaged laterality. 
     
     
         35 . The computer-implemented method of  claim 33 , wherein the one or more intended imaging attributes comprise an intended bodily structure, and wherein the one or more imaging attributes comprise an imaged bodily structure. 
     
     
         36 . The computer-implemented method of  claim 33 , wherein the one or more artificial intelligence modules comprise a convolutional neural network. 
     
     
         37 . The computer-implemented method of  claim 33 , wherein performance of the current image acquisition session using the first imaging modality is conditioned on detecting no discrepancies between the one or more imaging attributes and the one or more intended imaging attributes. 
     
     
         38 . The computer-implemented method of  claim 33 , wherein the second imaging modality comprises a visible light imaging modality. 
     
     
         39 . The computer-implemented method of  claim 33 , wherein the first imaging modality comprises a medical imaging modality. 
     
     
         40 . A system for preventing imaging errors, comprising:
 one or more processors; and   one or more hardware storage devices that store instructions that are executable by the one or more processors to configure the system to:
 prior to performing a current image acquisition session of a structure using a first imaging modality, capture one or more images of the structure using a second imaging modality that is different from the first imaging modality; 
 access an indication of one or more intended imaging attributes, the one or more intended imaging attributes being selected to be implemented for the current image acquisition session; 
 determine one or more imaging attributes by utilizing the one or more images of the structure of the second imaging modality as input to one or more modules configured to determine imaging attributes based upon input imagery, the one or more imaging attributes comprising output of the one or more modules; and 
 in response to determining one or more discrepancies between the one or more imaging attributes and the one or more intended imaging attributes (i) prevent performance of the current image acquisition session and/or (ii) present a notification on a user interface.