Trauma Fracture Detection

Purpose

Detect all fractures on CT chest, abdomen, and pelvis performed for trauma

Tag(s)

 

Panel

Musculoskeletal 

TOUCH-AI ID

TAI-05180018 

Originator

Dan Chernoff 

Panel Chair

Jay Patti 

Panel Reviewers

Musculoskeletal Panel 

License

Creative Commons 4.0 

Status Public Commenting
                               

Clinical Implementation


Value Proposition

Many fractures, some of which are obvious in retrospect, are missed on trauma CT.

Narrative(s)

A 25-year-old patient presents with blunt-force trauma from a fall off of a ladder. CT of the chest, abdomen, and pelvis is performed to determine if internal bleeding or organ injury is present. Fracture detection is generally less critical to immediate care of the patient and can be overlooked.

Workflow Description

The CT thin slice data set is preprocessed for bone segmentation and sent to the AI engine, which identifies likely fractures (unknown number) and marks these for extra scrutiny by the radiologist (analogous to computer-aided detection marks in mammography).

Considerations for Dataset Development


Procedures(s): {CT, Chest/Abdomen/Pelvis}

View(s): {AP, PA/Lat, inclination e.g., upright, semi-upright, supine}

 

Sex at Birth: {Male, Female}

 

Age: preference for skeletally mature

 

Anatomy Altering Conditions: {Diabetes, Charcot Joint}

 

Position: {weight bearing, non-weight bearing}

Technical Specifications


Inputs

DICOM Study

Procedure

CT, Chest/Abdomen/Pelvis

Views

AP, PA/Lat, inclination e.g., upright, semi-upright, supine

Data Type

DICOM

Modality

CT

Body Region

Chest, Abdomen, or Pelvis

 

Primary Outputs

Fracture Detection

RadElement ID

RDE264

Definition

Detect the presence of a fracture

Data Type

Categorical

Value Set

0-Unknown

1-Fracture

2-No Fracture

Units

N/A

Public Commenting


Use cases are meant to be a primary vehicle for distributing clinical information to the developer community. They pinpoint precise scenarios within radiology workflows where potential automation could add noticeable value and establish standards for interpreting and passing corresponding common data elements. Implementing effective standards requires the perspective from all stakeholders. So to that end, we encourage your feedback on use cases.

To submit comments, please email DSIUseCases@acr.org with the use case title(s) and relevant comments by January 1, 2019. If more convenient, you may also download this use case and comment directly on the PDF. Just attach the PDF copy on the email.