TIME-ORIENTED MULTI-IMAGE CASE HISTORY – WAY TO

THE “DISEASE IMAGE” ANALYSIS

Saveli Goldberg

, Nikita Shklovskiy-Kordi

and Boris Zingerman

MGH, Boston, MA, USA

National Center for Hematology, Moscow, RF

Keywords: Integrate Medical Image Construction, Image Knowledge Base.

Abstract: Electron patient records clinical database allows creating integrative presentation links different types of

medical information for individual patient on real-time scale. Resulting image, can be confront with

"disease image" – non-formalised physician’s perceptions of nosology expression in individual patient’s

disease. Normalization of parameters permits data processing based on case-to-case and case-to-cluster

comparative and multivariate statistical analysis of the patient’s data.

1 INTRODUCTION

Recognition of diagnostic pictures, uniting different

data and parameters in the process of deagnostic of

individual patient’s disease can be naimed the

creation of "disease image" (Vorobiev et al., 1999).

Connecting individual features of patient suffering

with general medical knowledge remains the main

problem of medicine. This mission will never be

solved completely at a tool level because Human

Being is the extreme quantum device for making

decisions of this kind of problems (Liberman et al.,

1998). However organazing data and images from

databases and collecting knowledge base about their

possible interaction is a real problem for experts and

should be studied by mathematic theory.

The verbal characteristic of creative thinking of a

doctor hematologist-morphologist experienced in

both macro and microscopic investigation of a

patient can be put into words: "You are telling me

nonsense about this patient! I do see - (hand wave in

the air) - the picture of his bone marrow!».

Analysis of disease progression and treatment

outcome requires great amount of data with

reference to their occurrence on the time scale and

causal connections. Most part of this data is

avaliable via computer, but there is no instrument to

extract them from different uncoordinated databases,

to organize them, or at least to collect them on single

screen. In this paper we propose a real-time

computerized medical system for collecting and

storage of clinical information on an individual

patient with abilities of immediate verification,

analysis and creation of integrated presentation

which can be analyzed as the image of the individual

case and “disease image”. The Sysem presents way

to reduse number of analised variables, selecting the

tamplete. The redused set of variables should retain

as much of important for treatment information

existed in the original variables as possible. Our

previose experience was to solve similar problem

with Dr. Watson Type System (Goldberg et al.,

1995).

2 MATERIALS AND METHODS

We have taken advantage of a traditional system

used for logical structuring of data known as

“temperature sheets”. This system leading

parameters and therapeutic assignments on one sheet

of observation have a common time axis. This

approach is common in Hematology/Oncology units

and it has helped a lot to develop clinical parameters

for treatment of acute radiation disease (biological

dosimetry, in particular) (Vorobiev, 1970), as well

as to improve protocols for treatment of

hematological malignancies (Vorobiev, 1977). We

used the following steps to generate this instrument:

1. Integration of data stored in different formats

(text, tables, roentgenograms, microphotographs,

videos etc.);

200

Goldberg S., Shklovskiy-Kordi N. and Zingerman B. (2007).

TIME-ORIENTED MULTI-IMAGE CASE HISTORY – WAY TO THE “DISEASE IMAGE” ANALYSIS.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications, pages 200-203

DOI: 10.5220/0002070902000203

 SciTePress

Figure 1: Example of the Page of Dynamic Observation of a patient underwent chemotherapy treatment. Some information

presented as marks only, some as small windows. The dynamics of chosen parameters (Temperature, WBC and Platelet

counts) are normalized and colour-coded.

2. Compression of clinical data by highlighting

important and urgent information;

3. Display of information in an integrated fashion

on the same screen;

Automatic matching of entered data with stored

timetables derived from established protocols for

diagnostic procedures and treatment.

Exit from the assigned limits of selected

parameters is similarly controlled.

The data can be plotted manually on the basis of

standard case history (wherein the physician

acquires medical or diagnostic information and

enters it manually on a Page of Dynamic

Observation (PDO)), or based on a Time-Oriented

Multi-Image Case History (TOMICH) (Shklovskiy-

Kordi et al., 1998).

Manual method of entering data on PDO does

not require additional devices or software besides a

regular PC computer with Microsoft Office and a

PDO template in an Excel format. PDO data entry

form is an Excel spreadsheet, in which certain rows

are reserved for specific type of information (i.e.

clinical data, laboratory results, medications, etc.).

The software contains a set of templates for widely

used clinical protocols and a system for automatic

detection of protocol violations.

When a standard protocol is used for

management of a patient with known diagnosis, a

template is provided with required laboratory data

and medications to be used with a timetable for their

administration.

The field of the graphical presentation of the

“dynamic» indicators - any numerical indicators

which change in the time (as temperature, blood

pressure, blood counts) essential for the clinical

case. The field of flags represent:

- icons which open physician notes,

-icons from which specific additional

information can be open . The color of these icons

corresponds to the expert evaluation of the

importance: “red” - changes the opinion about the

illness and lead to a change of the cure tactic;

“green” - confirms the diagnosis and the cure tactic;

“gray” - does not give a definite answer.

- icons of the planed research, which color

depends on the fulfilling.

After filling the “event form”, a cursor tags the

event to a “window” for a brief description (i.e. CT

TIME-ORIENTED MULTI-IMAGE CASE HISTORY – WAY TO THE “DISEASE IMAGE” ANALYSIS

201

scan description on Fig.1). A double click opens a

map of all pictures and text files linked to the event.

Normalization. All numeric clinical data is

broken into normal, sub-normal and pathological

range. This provides normalization of all parameters

and presentation on common axes. To define the

range of sub-normal values, a team of experts

empirically established the scope of “acceptable”

(for the given diagnosis and, in some cases, for an

individual patient) parameters. If a parameter stays

within the defined sub-normal or normal range, no

special action is required. If the specific value is out

of the acceptance limits, the program generates an

automatic alarm signal.

Complications: Complications are recorded on a

specific line in PDO and serves to visualize the

dynamics of patient’s symptoms. After a

symptom/syndrome is selected from a pre-loaded

list, a window appears on the screen with a

definition and criteria to assist in the diagnosis and

management. 4-positions of scale, “x” - lack of the

syndrome, “+” - the growth of the syndrome, “-“ -

the decreasing of the syndrome, “0” - the state

without any changes (stabilization).

3 DISCUSSION

TOMICH has a standard format for presenting key

components of patient’s medical record (the constant

form of a positional relationship of the basic

semantic units of a case history), but also has the

flexibility for adding new templates, as necessary for

a specific diagnosis. These templates accumulate

pre-defined lists of medications, required lab tests

and syndromes, and define sub-normal and

pathological range of values, as well as color palette

for drugs and graphs. Also, the template may refer to

the standard protocols for specific diseases or

clinical trials stored in the database. Normalization

of parameters makes future perspective of the data

processing based on case-to-case and case-to-cluster

comparative and multivariate statistical analysis of

the patient’s data

The beforehand constructed template permits

standard recognized images for diagnosis and helps

to discriminate general characteristics and specific

features for an individual patient. For example, there

are accepted criteria for decrease in platelets,

leukocyte and hemoglobin in response to

chemotherapeutic treatment. We found that

comparison of shapes of drug-dependent changes in

blood counts is a valuable estimation of outcome

(Shklovskiy-Kordi et al., 2004).

In a real-time mode, TOMICH automatically

performed data validation and notified a user when

selected parameters were beyond acceptable ranges

or when the timetable set by the protocol was not

followed. These software features permit health care

personnel to monitor and correct, when needed,

individual actions taken by medical personnel.

TOMICH links the actions of medical staff with

requirements set by the protocols. Attention of

physicians and staff is prompted by a color indicator

(Shklovskiy-Kordi et al., 2003).

4 CONCLUSIONS

TOMICH is a convenient and easily automated

method for entering all available information about a

patient. It may be classified as a decision-support

and expert–oriented system, which allows a

physician to select a pre-entered template and to

modify it for creating the most appropriate template

for a particular patient. It provides easy access to

primary data and allows generation of a common

time-line axis format for multimedia presentation of

a patient’s record. The system links different

medical information and forms a cognate image of

diseases. This presentation allows real-time

evaluation of disease and of the response to

treatment. Use of TOMICH facilitates the analysis

of clinical course and compliance and reduces the

probability of medical errors.

Normalization of parameters makes future

perspective of the data processing based on case-to-

case and case-to-cluster comparative and

multivariate statistical analysis of the patient’s data

ACKNOWLEDGEMENTS

This work was supported, in part, by a Grants 05-07-

90231-в and 06-08-00610-а of RFBR, Russia.

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