<SCRIPT LANGUAGE="JavaScript1.1" SRC="http://ads.medscape.com/js.ng/Params.richmedia%3Dyes%26ord%3D1063426927893%26site%3Dmscp%26cb%3Dmed%26pos%3Dtop%26size%3D468x72%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0"></SCRIPT> <NOSCRIPT><A HREF="http://ads.medscape.com/click.ng/Params.richmedia%3Dyes%26ord%3D1063426927893%26site%3Dmscp%26cb%3Dmed%26pos%3Dtop%26size%3D468x72%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0"><IMG SRC="http://ads.medscape.com/image.ng/Params.richmedia%3Dyes%26ord%3D1063426927893%26site%3Dmscp%26cb%3Dmed%26pos%3Dtop%26size%3D468x72%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0" BORDER="0" WIDTH="468" HEIGHT="72"></A></NOSCRIPT>

My Homepage|Log Out

September 13, 2003

|

Next Page

|

|

From Medscape General Medicine™

Pervasive Clinical Computing

Visualization Tools

Posted 09/12/2003

Bryan Bergeron, MD

Introduction

The practice of clinical medicine relies heavily on pattern recognition -- the ability to recognize patterns in the presentation of patient data and thereby formulate a diagnosis. The focus of a clinician's pattern recognition skills ranges from a patient's initial presentation and data derived from laboratory tests to electrocardiographic strips to radiographic studies.

One of the purposes of clinical computing is to transform patient data to a form that facilitates pattern recognition. For example, tabular listings of laboratory data often can be more rapidly interpreted when the data are plotted relative to normal ranges, as shown in Figure 1. This technique can be used in traditional paper charts as well as electronic medical record (EMR) systems.

Figure 1. (click image to zoom) Transformation of tabulated clinical data to graphical plots is an often-used visualization enhancement supported by many EMR systems.

Although simple transformations of tabular data are nice to have, more advanced visualization techniques are often absolute necessities in the fields of neurology, psychiatry, and neurosurgery. For example, 3-dimensional (3-D) visualization technologies such as functional magnetic resonance imaging (fMRI) are supplanting the electroencephalograph, which appears as indiscernible squiggles to most clinicians. Functional MRI, which is based on the nuclear magnetic resonance of protons to produce proton density maps, empowers clinicians to observe brain activity as 3-D color images. Easily recognizable patterns of activity are associated with personality traits from aggression and risk-aversion to depression. With fMRI, clinicians with only minimal knowledge of neuroanatomy and neurophysiology can quickly recognize patterns of color on the brain surface and correlate the patterns with a patient's mental activity. Three-dimensional visualization tools can be used with MRI, computed tomography (CT), and other clinical imaging data to plan surgical approaches and therapies.

Both plotting and graphing of laboratory data and the 3-D rendering of imaging data have clinical value by making clinical data more accessible. As such, visualization tools are a critical component of pervasive computing in that this technology constitutes the final link between wireless connectivity, tablet PC, and other computer components and the clinician end user. This article reviews readily available software tools for clinical data analysis and interpretation as well as the latest systems in medical research institutions.

Statistical Data Visualization

Statistical analysis of clinical data is a major component of medical research. The 2 basic approaches to visualizing clinical data in databases and spreadsheets are to either buy add-ons to a standard office package or to use a dedicated software program. Add-ons to industry standard programs, such as Excel, are an inexpensive approach that obviates the need to copy information from Excel and paste it into another application. An example of an Excel add-on is the Analyze-it program that adds statistical analysis and visualization to Excel. The $100 package provides modest visualization capabilities for small datasets that can be handled by the basic Excel spreadsheet.
Dedicated visualization packages, such as products from SPSS and Mathsoft, typically are more powerful, have a longer learning curve, and are usually more expensive than simple add-ons. For example, SPSS offers a variety of statistical analysis and visualization packages, ranging from single-user programs that sell for about $150 to high-end Web-based applications that can be connected to an EMR system. Mathsoft's Mathcad program provides advanced tools for analyzing and visualizing clinical data, such as laboratory data, and creating a variety of 2-D and 3-D graphics (Figure 2). Mathcad, which is marketed to engineers and scientists, is more versatile and provides a host of interactive visualization tools. Nonacademic prices start at about $1000 for a single-user license.

Figure 2. (click image to zoom) Multiple variable 3-D visualization produced with Mathcad

Data Mining

One of the most useful applications of visualization tools in clinical and research medicine is to view the results of data mining, which is searching for unknown relationships between data in large clinical databases. Data mining is the basis for establishing treatment standards, insurance fee structures, and for discovering recent and evolving bacterial and viral infections. Examples of companies that offer visualization tools optimized for data-mining applications include SAS, Minitab, and Advanced Visual Systems. SAS is an enterprise solution marketed at large research institutions, including academic teaching centers. Among SAS's offerings are a text data-mining utility that can scan large clinical databases to discover trends and patterns.
OpenViz from Advanced Visual Systems is a large-capacity data-mining analysis and visualization system that is an enterprise-level program like SAS, which is intended for mining large databases and is priced accordingly. OpenViz generates graphics based on the real-time analysis of data mined from large databases for display on a variety of hardware platforms as well as in a standard Web browser.

Minitab, which is intended to work with relatively small, PC-based databases, is less expensive than SAS or OpenViz, at about $1000 for a single nonacademic license. Statistical and visualization capabilities are similar to those offered by SPSS and Mathcad. However, Minitab is designed to work effectively with as many as several thousand datasets at a time as opposed to the tens of thousands that can be handled by a large system, such as SAS or OpenViz.

Illustration

Visualization tools are often essential for providing illustrations for the teaching and learning of clinical medicine. In addition to visualizing laboratory data in graphical form, visuals are useful in supplementing textual descriptions of surgical procedures and medical conditions for residents, other clinicians, or patients.
An example of a product in this category is A.D.A.M. Interactive Anatomy. The interactive 3-D atlas, which sells for about $500 for an individual license, can be used with courseware and presentations. A similar product, limited to the skeletal system, is Virtual Anatomy's 3-D Skeletal System. Although the visuals are limited to the skeletal system, the individual bones are rendered in very high resolution suitable for publication or presentation. The 3-D Skeletal System sells for about $130 for individual licenses. A good source for these and other medical illustration packages is Continuing Medical Education,

Academic Research

The National Library of Medicine's Virtual Human Project, in which male and female cadavers were fixed, sliced, and digitized for medical education and research purposes, forms the basis for a dozens of commercial and academic visualization tools. The National Library of Medicine's AnatQuest site offers a library of annotated cadaver slices (Figure 3) as well as 3-D images created from the digitized slice data (Figure 4).

Figure 3. (click image to zoom) Interface to the National Library of Medicine's AnatQuest program, which allows users to select interactively the slice displayed.

An educational application of the Virtual Human image data can be viewed at the University of Utah's Medstat site, which is intended for use by medical students studying anatomy. Researchers in the Department of Anatomy at The Wright State University School of Medicine used the Virtual Human data to create Beyond Vesalius, a free downloadable set of interactive programs designed to teach sectional and radiologic anatomy.

Figure 4. (click image to zoom) Rendering of the human heart from the National Library of Medicine's AnatQuest site

The Colorado State University Center for Human Simulation uses data from the Virtual Human Project as the basis for a 3-D virtual surgical simulation. Users can direct a virtual scalpel across an area, and the simulator displays the correct incision. The scalpel appears to cut through the skin and underlying tissue, allowing the fat, muscles, and blood vessels to be revealed.
The Center for Human Simulation at the University of Colorado is exploring visualization as a means of teaching functional anatomy of the Virtual Human dataset. Additional projects related to the Virtual Human Project are listed on the National Library of Medicine's site.

The Biomedical Imaging Resource (BIR) at Mayo Clinic offers a variety of software packages for the analysis and manipulation of clinical image data from CT and MRI images. The visualization software is available without charge for selected academic applications and also is sold commercially to others. Additional applications of medical visualization technology to clinical study data can be seen at the Georgia Institute of Technology Biomedical Imaging and Visualization site. Similarly, Stanford University's Digital Human Laboratory is researching cardiovascular and neuromuscular visualization technologies in diagnosis and treatment.

The Genomics Connection

Visualization is one of the most active areas of research in genomics and clinical genetics. While the visualization requirements of medical researchers range from the 3-D rendering of protein structures to modeling protein-protein interactions in real time, clinical geneticists are concerned more with visualizing chromosome-level anomalies as a component of screening and as a teaching aid in counseling patients.
Cn3D is a free protein visualization program from the National Center for Biotechnology Information that enables medical researchers to transform tabular data from one of the Internet-based genomics databases, such as the Protein Databank, into 3-D structures (Figure 5).

Figure 5. (click image to zoom) The structure of Insulin, as visualized by Cn3D, a free program available online from the National Center for Biotechnology Information.

In addition to visualizing protein structures, more clinically relevant visualization applications in genomics include the generation of gene-expression matrices from gene-chip data (Figure 6). In creating a gene-expression matrix from the patterns read from a gene chip, proteins created by the body in response to specific treatments are arranged to illustrate relative response to treatments.

Figure 6. (click image to zoom) Visualization of the responses measured by a gene chip with the aid of an expression matrix.

The challenge now for the clinical bioinformatics community is to devise visualization techniques, such as the gene-expression matrix, that are not only intuitive and useful at the molecular and chromosome levels but that highlight clinically relevant information. Such techniques could enhance research and education efforts, further facilitating the transformation of patient data to pattern recognition, and benefiting both clinicians and patients in the process.

Section 1 of 6

Next Page: Statistical Data Visualization

Bryan Bergeron, MD, Assistant Professor and Research Affiliate, Health Sciences and Technology Division, Harvard Medical School and Massachusetts Institute of Technology, is a Fellow of the American College of Medical Informatics and the author of numerous books and several hundred articles in the field of biomedical informatics. His latest book, Essentials of Knowledge Management, includes a discussion of how technology can best be applied to managing data.

Medscape General Medicine 5(3), 2003. © 2003 Medscape

Related Resources

Clinical articles on this topic

Join a Discussion with your colleagues

<SCRIPT LANGUAGE="JavaScript1.1" SRC="http://ads.medscape.com/js.ng/Params.richmedia%3Dyes%26ord%3D1063426927906%26site%3Dmscp%26cb%3Dmed%26size%3D120x600%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0"></SCRIPT> <NOSCRIPT><A HREF="http://ads.medscape.com/click.ng/Params.richmedia%3Dyes%26ord%3D1063426927906%26site%3Dmscp%26cb%3Dmed%26size%3D120x600%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0"><IMG SRC="http://ads.medscape.com/image.ng/Params.richmedia%3Dyes%26ord%3D1063426927906%26site%3Dmscp%26cb%3Dmed%26size%3D120x600%26zip%3D92083%26ct%3Dus%26pf%3D18%26sp%3D0" BORDER="0" WIDTH="120" HEIGHT="600"></A></NOSCRIPT>

• | About Medscape | • | Privacy & Ethics | • | Terms of Use | • | Help | • | WebMD Health |

All material on this website is protected by copyright, Copyright © 1994-2003 by Medscape. This website also contains material copyrighted by 3rd parties. Medscape requires Netscape or Microsoft browsers in versions 5 or higher.