Speech Assisted Radiology System for Retrieval, Reporting and Annotaiton

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Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

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

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Outline


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


Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Introduction


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


Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Introduction


Status quo (simplified)

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Introduction


Status quo (simplified)

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Definitions


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

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Voice Directed Search


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.



Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Voice Directed Search


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?

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Parsing Spoken Text


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:

There is moderate disc bulging at L5/S1

Size

Small

Small to Moderate

Moderate

Moderate to Large

Large

There

is

moderate

disc

bulging

at

L5/S1

Diagnosis

Disc bulging

Herniated disc

Degenerative disc disease

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Voice Directed Search


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



Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Structured Reporting


Image Embedding


Findings


Impressions


Annotations


Text, descriptors, drawings


Become linked with the image(s)


Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Experiments


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)




Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Experiments


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

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Results

Data points and their linear regression lines

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Results

Change in accuracy for each paradigm

Method: (SRM
-
PEM)/SRM


Paradigm

Test

Accuracy (area)

Change in Accuracy

Distance

Speech (SRM)

87.65

+10.0285%


Parsing (PRM)

96.44

Weighted

Speech (SRM)

66.93

+41.8945%


Parsing (PRM)

94.97

All
-
or
-
Not

Speech (SRM)

35.81

+96.7607%


Parsing (PRM)

70.46

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Current domains of implementation
(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.

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Demo

Kansas State University

Computing and Information Sciences

IEEE CBMS Conference

August 3
-
4, 2009

Questions?

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.

surya@aidentitymatrix.com