Tarsometatarsal Joint Status

Purpose

Detect if Lisfranc joint is normal or abnormal

Tag(s)

 

Panel

 Musculoskeletal

TOUCH-AI ID

 TAI-05180017

Originator

 Musculoskeletal Panel

Panel Chair

 Jay Patti

Panel Reviewers

 Musculoskeletal Panel

License

 Creative Commons 4.0

Status  Public Commenting

Clinical Implementation


Value Proposition

Injury is common but easily missed by nonspecialists. Nonspecialists would benefit from an algorithm that segments the joint and detects abnormality. AI meeting this use case would help to reduce the false negative rate, patient risk, and the medical legal risk for the radiologists.

Narrative(s)

A 25-year-old patient presents with foot trauma and an x-ray is taken in the emergency room. An algorithm evaluates the image and categorizes the joint as normal, abnormal, or undetermined. The radiologist is informed of this categorization at the time of interpretation.

Workflow Description

An image is obtained from a modality and sent to PACS and the AI engine. The image is analyzed by the engine. The system categorizes the Lisfranc joint as normal, abnormal, or uncertain. A message is sent to PACS from the engine with the classification information. If the location of the joint is also identified, the location information can also be sent to PACS to highlight the region the engine identified.

Considerations for Dataset Development


Procedures(s): X-ray, Lower Extremity

 

View(s): {AP, Oblique, Lateral}

 

Sex at Birth: {Male, Female}

 

Age: [15,90]

 

Anatomy Altering Conditions: {Diabetes, Charcot Joint}

 

Position: {weight bearing, non-weight bearing}

 

Weight: varied

Technical Specifications


Inputs

DICOM Study

Procedure

XRAY, Lower Extremity

Views

AP, Oblique, Lateral

Data Type

DICOM

Modality

XRAY

Body Region

Lower Extremity

Anatomic Focus

Foot

 

Primary Outputs

Lisfranc Joint Status

RadElement ID

RDE263 

Definition

Status of Lisfranc joint

Data Type

Categorical

Value Set

0–Unknown

1–Normal

2–Abnormal

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.