Speech assisted radiology system for retrieval reporting and annotaiton
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Speech Assisted Radiology System for Retrieval, Reporting and Annotaiton. Tim Weninger , Daniel Greene, Jack Hart, William H. Hsu and Surya Ramachandran*. Department of Computing and Information Sciences Kansas State University, Manhattan KS *AIdentity Matrix Inc, Elmhurst, IL.

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Speech assisted radiology system for retrieval reporting and annotaiton

Speech Assisted Radiology System for Retrieval, Reporting and Annotaiton

Tim Weninger, Daniel Greene, Jack Hart, William H. Hsu and Surya Ramachandran*

Department of Computing and Information Sciences

Kansas State University, Manhattan KS

*AIdentity Matrix Inc, Elmhurst, IL

2009 IEEE International Symposium on Computer-Based Medical Systems

Albuquerque, NM, USA


Outline
Outline and Annotaiton

  • Introduction

    • Motivation

    • Example

  • Voice Directed Search

    • Prerequisites

    • Parsing Spoken Text

    • Search

    • Findings and Impressions

  • Merit Case Client

    • Experiments

      • Metrics

    • Results

  • Conclusions and Future Work

  • Demo


Introduction
Introduction and Annotaiton

Motivation

Paradigm: Radiology

Healthcare is expensive

Why?

Errors

2004-2006 Medicare study

Errors cost US$8.8 billon

University of Baylor study:

Out of 113 errors studied

Transcription was the base-cause for 46%

(Seely et al. 2004)

Inefficiencies

Medical Transcription

Adds cost

Adds complexity


Introduction1
Introduction and Annotaiton

Status quo (simplified)


Introduction2
Introduction and Annotaiton

Status quo (simplified)


Definitions
Definitions and Annotaiton

Define our Terms:

Paradigm: Radiology

MRI

Finding/Impression

Medical diagnostic interpretation of particular abnormalities as seen by the radiologist

Annotation

The expression of a medical opinion related to a specific image.

Drawn Arrow

Circle

Etc

Merit Case Client:

Speech directed PACS system


Voice directed search
Voice Directed Search and Annotaiton

Current PACS systems

Example: find men with slipped discs

“Search. sex equals male. diagnosis equals herniated disc.”

[Search] is a command

[male] is a menu option in list [sex]

[Herniated disc] is option in list [diagnosis]

Disadvantages:

Narrow speech scope

Voice recognition systems are not foolproof

Example: Homonyms

“Search. Sex equals mail. Diagnosis equals herniated disk.”

Does not compute!

Main advantage:

Capable of standardizing naturally spoken medical terminologies with significant degrees of variance.


Voice directed search1
Voice Directed Search and Annotaiton

Example:

Find all male patients between the ages of 55 and 60 with a slipped disc in the L4/L5 region with no previous history of disc injury.

“Find men with a slipped disc in the L4/L5 region”

[Find] is a command along with others

[male] is a interpreted to be [male] within [sex]

[between 55 and 60] is [55-60] within [age]

[slipped disc] is interpreted to be [herniated disc] within [disease]

[disc injury] looks for any [disc] within [disease]

Moreover:

This widens the search scope

Voice recognition systems are not foolproof

Example: Homonyms and formatting

“Find men with a slipped disk in the El four slash El five region.”

This works as well.

How?


Parsing spoken text
Parsing Spoken Text and Annotaiton

Operating Assumptions:

The system maintains a complete list of all ages, sexes, diseases, etc. i.e. type enumeration

Valid responses are available in lists

Homonyms do not coexist in a list

If so, then it’s hard to make a decision

Goal

Map what is dictated to the appropriate descriptor

Sliding window approach:

Size

Diagnosis

Small

Disc bulging

Herniated disc

Small to Moderate

Degenerative disc disease

There

Moderate

is

moderate

disc

bulging

at

L5/S1

Moderate to Large

Large

There is moderate disc bulging at L5/S1


Voice directed search2
Voice Directed Search and Annotaiton

Synonym Learning

How does the system know:

“Slipped Disc” = “Herniated Disc” = “Disc Herniation”

The system will make an initial guess.

System will not initially recognize “Slipped Disc”

System remembers corrections

Correction process is easy

Learns speakers word choice preference


Structured reporting
Structured Reporting and Annotaiton

Image Embedding

Findings

Impressions

Annotations

Text, descriptors, drawings

Become linked with the image(s)


Experiments
Experiments and Annotaiton

Data points

(1) Text read by the radiologist

(2) Text output by speech recognition engine

(3) Descriptors filled in by Merit Case Client

(4) Correct state of the descriptor (ground truth)

Metrics

Speech Recognition Metric (SRM)

Word-Edit distance between original text (1) and output by the speech recognition system (2).

Parsing Engine Metric (PEM)

Word-Edit distance between menus filled in by Merit Case Client (3) and the correct answer (4)


Experiments1
Experiments and Annotaiton

Reporting and Analysis

Some errors are more costly than others

3 reporting methods:

Word distance

Weighted errors

Disease descriptor= 60%

Location descriptor = 20%

All others descriptors = 20%

All or not

Was it completely correct or not?

Experiment

Radiologist (Dr. Schekall, MD) made 100 dictations based on real-world cases

25 search queries

75 findings and impressions dictations

No re-dos allowed

Speech recognition system was NOT pre-trained


Results and Annotaiton

Data points and their linear regression lines


Results
Results and Annotaiton

Change in accuracy for each paradigmMethod: (SRM-PEM)/SRM


Current domains of implementation ongoing
Current domains of implementation and Annotaiton(ongoing)

  • Branded under - Virtual Integrity in Medicine TM (VIM)

  • Electronic Medical Records

    • VIM Radiology

      • PET, CT, MRI, Nuclear, X-Ray, Ultrasound, etc

    • VIM Cardiology

      • ECG, Ultrasound, CT, Nuclear, Cath lab, Vitals, Resting, Exercise, Stress, Ambulatory BP and Spirometry

    • VIM Neurology

      • From out-patient clinical through surgery

  • Front & Back Office

    • Scheduling, Patient profile, Insurance, Rule-outs ICD9/10, Referring Physician, Reporting, Billing & Accounts bridge, Clinical messaging, etc.


Demo and Annotaiton


Questions

Questions? and Annotaiton

Special thanks -

Dr. Michael Schekall, MD

Deborah Templeton, BS, CNMT, RT(R), LRT

Hutchinson Clinic PA, Hutchinson, KS

Jeff Barber, Andrew Walters

Kansas State University

Industry Contact for more information –

Surya Ramachandran

AIdentity Matrix Medical Inc.

[email protected]


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