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PURPOSE: To examine students' learning before and after revising an educational software program and to explore students' patterns of use of an interactive feature that compares images. METHOD: Study participants were 466 University of Washington School of Medicine students. Two cohorts of students (one in 1996 and one in 1997) used the original version of the software. Following analysis of the students' learning, the software program was modified based on instructional design principles pertaining to visual learning and concept acquisition. A 1998 cohort of students used the revised program and their performance was compared with that of the 1996 cohort. Analyses were based on pre- and post-test scores, data collected from the observation of students, and navigational pathways tracked by the program. RESULTS: There was very little difference in the overall performances of the students who used the original program and those who used the revised program. Error analysis focusing on 11 conceptual areas showed that reductions in errors occurred for six of 11 concepts, with statistically significant reductions of errors for two concepts. Additional navigational data collected in 1998 showed that students used an interactive feature for comparing images in different patterns. The data showed a positive association between performance and the anchored viewing mode of image display. CONCLUSIONS: While this study cannot point to specific design components that facilitated or hindered learning, it demonstrated a potential benefit of linking usage-pattern data and performance. Future studies should evaluate design factors that affect usage patterns and performances based on navigational data collected while students interact with software programs.

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BACKGROUND: The microscopic examination of urine sediment is one of the most commonly performed microscope-based laboratory tests, but despite its widespread use, there has been no detailed study of the competency of medical technologists in performing this test. One reason for this is the lack of an effective competency assessment tool that can be applied uniformly across an institution. METHODS: This study describes the development and implementation of a computer program, Urinalysis-ReviewTM, which periodically tests competency in microscopic urinalysis and then summarizes individual and group test results. In this study, eight Urinalysis-Review exams were administered over 2 years to medical technologists (mean, 58 technologists per exam; range, 44-77) at our academic medical center. The eight exams contained 80 test questions, consisting of 72 structure identification questions and 8 quantification questions. The 72 structure questions required the identification of 134 urine sediment structures consisting of 63 examples of cells, 25 of casts, 18 of normal crystals, 8 of abnormal crystals, and 20 of organisms or artifacts. RESULTS: Overall, the medical technologists correctly identified 84% of cells, 72% of casts, 79% of normal crystals, 65% of abnormal crystals, and 81% of organisms and artifacts, and correctly answered 89% of the quantification questions. The results are probably a slight underestimate of competency because the images were analyzed without the knowledge of urine chemistry results. CONCLUSIONS: The study shows the feasibility of using a computer program for competency assessment in the clinical laboratory. In addition, the study establishes baseline measurements of competency that other laboratories can use for comparison, and which we will use in future studies that measure the effect of continuing education efforts in microscopic urinalysis.

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The microscopic examination of urine sediment is a common diagnostic tool taught to medical students, medical technologists, and others. The urine microscopic exam is difficult to teach because supervised instruction and textbook-based teaching suffer from numerous drawbacks. Here, we describe Urinalysis-Tutor, a computer program that uses digitized microscope images and computer-based teaching techniques to systematically teach the urine microscopic exam. In addition, we report the results of a 2-year study that evaluated the effectiveness of the program in 314 second year medical students who were required to use the program. The program contained two, 20-question exams. In the first year of the study (1996), one of the exams was chosen as the pretest and the other as the posttest; the pretest had to be completed before the students viewed the contents of the program, and the posttest was taken after finishing the tutorial. In 1997, the order of the two exams was reversed. In 1996, 159 students completed the study. The mean pretest score was 34% (SD, 14%), the mean posttest score was 71% (SD, 13%), and the improvement was significant (P <0.001, paired t-test). In 1997, 155 students participated. The mean pretest score was 41% (SD, 11%), the mean posttest score was 71% (SD, 13%), and the improvement was significant (P <0.001, paired t-test). The study shows that Urinalysis-Tutor helps medical students learn to interpret the microscopic appearance of urine sediment and that it is feasible to implement this tutorial in a medical school class.

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The interpretation of peripheral blood smears has an important role in the diagnosis of hematologic diseases and is, therefore, part of the education of physicians and technologists. We describe a computer program, PeripheralBlood-Tutor (Lippincott-Raven, Philadelphia, Pa), that teaches the morphologic features of normal and abnormal peripheral blood smears; we also describe the evaluation of the effectiveness of the program in 133 second-year medical students who were required to use the program in their hematology course. The version of the PeripheralBlood-Tutor used in the study had 2 distinct but equivalent 20-question examinations; one examination, the pretest, was taken before the students viewed the contents of the program, and the other examination, the posttest, was taken after completing the program. The mean score on the pretest was 61% (SD, 14%), the mean on the posttest was 91% (SD, 10%), and the improvement was significant. In addition, 4 questions about peripheral blood smears, which were based on printed images, were administered at the end of the hematology course. The students scored an average of 2.75 (SD, 0.86), and a positive correlation was found between these scores and the scores on the Tutor posttest. The results of the study suggest that PeripheralBlood-Tutor is feasible to implement, and it helps students learn to interpret peripheral blood smears. The use of PeripheralBlood-Tutor is now a requirement in the medical school curriculum, the medical technology program, and the pathology residency at the University of Washington, Seattle.

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Despite the clinical relevance of the nails, the interpretation of the appearance of the nails is often neglected by clinicians and in medical training programs. To help overcome this we developed Nail-Tutor(TM), a personal computer program based on approximately 150 images that systematically teaches the anatomy, patterns of pathology, and diseases of the nails and tests the user's understanding of the material in the program. Nail-Tutor(TM) is intended for medical students, residents, other healthcare trainees, general practitioners, and dermatologists and requires approximately 3 hours to use. The program has a number of advantages over textbook-based instruction and supervised instruction. It does not require an instructor or textbook. In addition it uses animation to show an important anatomic relationship, graphic overlays to unambiguously point out key features of images, it can show key images side by side for comparison, and an examination to evaluate learning objectives. The program can be used as a stand-alone teaching module, a supplement to traditional instruction, a source for continuing education, or as a reference.

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The microscope-based immunofluorescence assay for anti-neutrophil cytoplasmic antibodies (ANCAs) is used in the evaluation of patients with Wegener's granulomatosis and related forms of vasculitis. We developed an interactive personal computer program, ANCA-Tutor, that introduces users to the assay and examines their understanding of the test. The program runs under Microsoft Windows and is based on processed digital images of human neutrophils stained in a standard immunofluorescence test. The images contain descriptive text overlays. The program contains a library of reference images and strategies for distinguishing between staining patterns.

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Our laboratory has been developing computer programs that help medical technologists improve their performance of the microscope-based immunofluorescence assay for antinuclear antibodies (ANA). This image-based laboratory test has been associated with poor reproducibility. We have previously described our first program, ANA-Tutor, which systematically teaches the ANA test by using approximately 150 processed digital images of ANA test results. The program we describe here, Pattern Plus Auditor, is a logical extension to ANA-Tutor. Pattern Plus Auditor tests the ability of laboratory personnel to interpret the ANA test, and tracks individual and laboratory performance over time. The program consists of image-based questions that test a variety of ANA staining patterns, including homogeneous, speckled, centromere, nucleolar, mixed patterns, and rare patterns. For each question, the program provides correct answers with explanations and color overlays that highlight key image features. By entering the proper password, users gain access to exam results for individuals and for the laboratory as a whole. Results are available for the current exam, any previous exam, or cumulatively on all exams to date. Intralaboratory testing with computer programs such as Pattern Plus Auditor might be a useful part of quality-assurance procedures for many image-based laboratory tests.

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High-resolution protein electrophoresis of serum, urine, and cerebrospinal fluid (CSF) can aid in the diagnosis of multiple myeloma, amyloidosis, macroglobulinemia, multiple sclerosis, and other diseases. Electrophoresis-Tutor is a personal computer program based on approximately 150 digital images that teaches the clinical interpretation of agarose gel electrophoretic patterns. The program is divided into the following sections: introduction, CSF, serum, urine, review of disease states, program navigator, and final exam. The CSF section describes normal and abnormal CSF findings with emphasis on oligoclonal banding, as seen in the CSF of patients with multiple sclerosis. The serum section emphasizes monoclonal gammopathy patterns but also has detailed descriptions of inflammation, liver disease, protein-losing disorders, genetic deficiencies, and other patterns. Monoclonal gammopathy is described in the context of specific associated clinical conditions (e.g., myeloma, amyloidosis). For each monoclonal gammopathy example, results of standard electrophoresis, densitometry, and immunofixation are presented. The review of disease states uses animation to illustrate the development and remission of a variety of pathological patterns. The program navigator allows the user to jump quickly to any place in the program. The optional exam contains 20 questions, and detailed feedback is given after each question. Electrophoresis-Tutor can be used as a stand-alone teaching tool, a companion to traditional instruction, or a reference source.

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The clinical laboratory's use of computers has evolved beyond the single minicomputer stand-alone system. Our laboratory information system is now part of an institutional network. The laboratory also uses smaller systems and workstations for a wide variety of functions, often with much data duplication among systems. We have been developing a network-based virtual database for laboratory test information. This system uses World Wide Web standards for hypertext and multimedia displays, which allows for the display of information retrieved from various department computer sources without the necessity of data duplication, modification of existing systems, or centralization of data. The medical technologists can continue to write testing procedures on their word processors. Maintenance of reference values, specimen requirements, etc., can continue as a laboratory information system function. Yet information from all of these disparate sources can be viewed in a consolidated format that has platform independence.

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Antibodies to cytoplasmic antigens can be identified with a microscope-based indirect immunofluorescence assay that uses a mouse stomach-kidney substrate. The antibodies are diagnostic markers in chronic active hepatitis, primary biliary cirrhosis, pernicious anemia, and other autoimmune diseases. We describe the development and features of an image-based computer program for teaching medical technologists and other health care workers the proper interpretation of cytoplasmic fluorescence staining patterns. The program, called Cytoplasmic Tutor, is written in Microsoft Visual Basic for Windows and runs on an 80486 microcomputer. it is based on a library of digital images, with key features described by overlays of text. The images were collected and processed with a computer-based fluorescence video microscopy system assembled in our laboratory.

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OBJECTIVE: Neural networks are a group of computer-based pattern recognition methods that have recently been applied to clinical diagnosis and classification. In this study, we applied one type of neural network, the backpropagation network, to the diagnostic classification of giant cell arteritis (GCA). METHODS: The analysis was performed on the 807 cases in the vasculitis database of the American College of Rheumatology. Classification was based on the 8 clinical criteria previously used for classification of this data set: 1) age > or = 50 years, 2) new localized headache, 3) temporal artery tenderness or decrease in temporal artery pulse, 4) polymyalgia rheumatica, 5) abnormal result on artery biopsy, 6) erythrocyte sedimentation rate > or = 50 mm/hour, 7) scalp tenderness or nodules, and 8) claudication of the jaw, of the tongue, or on swallowing. To avoid overtraining, network training was terminated when the generalization error reached a minimum. True cross-validation classification rates were obtained. RESULTS: Neural networks correctly classified 94.4% of the GCA cases (n = 214) and 91.9% of the other vasculitis cases (n = 593). In comparison, classification trees correctly classified 91.6% of the GCA cases and 93.4% of the other vasculitis cases. Neural nets and classification trees were compared by receiver operating characteristic (ROC) analysis. The ROC curves for the two methods crossed, indicating that the better classification method depended on the choice of decision threshold. At a decision threshold that gave equal costs to percentage increases in false-positive and false-negative results, the methods were not significantly different in their performance (P = 0.45). CONCLUSION: Neural networks are a potentially useful method for developing diagnostic classification rules from clinical data.

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Backpropagation neural networks are a computer-based pattern-recognition method that has been applied to the interpretation of clinical data. Unlike rule-based pattern recognition, backpropagation networks learn by being repetitively trained with examples of the patterns to be differentiated. We describe and analyze the phenomenon of overtraining in backpropagation networks. Overtraining refers to the reduction in generalization ability that can occur as networks are trained. The clinical application we used was the differentiation of giant cell arteritis (GCA) from other forms of vasculitis (OTH) based on results for 807 patients (593 OTH, 214 GCA) and eight clinical predictor variables. The 807 cases were randomly assigned to either a training set with 404 cases or to a cross-validation set with the remaining 403 cases. The cross-validation set was used to monitor generalization during training. Results were obtained for eight networks, each derived from a different random assignment of the 807 cases. Training error monotonically decreased during training. In contrast, the cross-validation error usually reached a minimum early in training while the training error was still decreasing. Training beyond the minimum cross-validation error was associated with an increased cross-validation error. The shape of the cross-validation error curve and the point during training corresponding to the minimum cross-validation error varied with the composition of the data sets and the training conditions. The study indicates that training error is not a reliable indicator of a network's ability to generalize. To find the point during training when a network generalizes best, one must monitor cross-validation error separately.

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Neural networks are a group of computer-based pattern recognition technologies that have been applied to problems in clinical diagnosis. This review focuses on one member of the group of neural networks, the backpropagation network. The steps in creating a backpropagation network are (1) collecting adequate training facts, (2) choosing the specific network structure, (3) training the network, and (4) cross-validating the trained network. The first published applications of backpropagation networks to problems in pathology and laboratory medicine have appeared recently. These applications are in the areas of image analysis and interpretation of laboratory results, and they demonstrate the feasibility of the approach.

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Neural networks are a relatively new method of multivariate analysis. The purpose of this study was to investigate the ability of neural networks to differentiate benign from malignant breast conditions on the basis of the pattern of nine variables: patient age, total cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A-I, apolipoprotein B, albumin, the tumor marker CA15-3, and the Fossel index (measurement of methylene and methyl line-widths in proton NMR spectra). The laboratory analyses were made with blood plasma or serum specimens. The neural network was "trained" with 57 patients: 23 patients with breast malignancies and 34 patients with benign breast conditions. A neural network with nine input neurons, 15 hidden neurons, and two output neurons correctly classified all 57 patients. The ability of the network to predict the diagnoses of patients that it had no encountered in training was tested with a separate group (cross-validation group) of 20 patients. The network correctly predicted the diagnoses for 80% of these patients. For comparison we analyzed the same sets of 57 training patients and 20 cross-validation patients by quadratic discriminant function analysis. The quadratic discriminant function, calculated from the same 57 patients used to train the neural network, correctly classified 84% of the 57 patients, and correctly diagnosed 75% of the 20 cross-validation patients. The results suggest that neural networks are a potentially useful multivariate method for optimizing the diagnostic utility of laboratory data.

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In the presence of Ba++, an increase in the bath HCO3- at constant CO2 (i.e., variable bath pH) produced a hyperpolarization. The hyperpolarizing effect of adding HCO3-/CO2 at constant bath pH was not significantly affected by the presence of 50 mumol/l strophanthidin. In the absence of Ba++, addition of HCO3-/CO2 at constant bath pH produced a Na(+)-dependent hyperpolarization. Therefore, CO2 movements, electrogenic Na+/K+ pump activity and changes in Ba++ binding do not contribute significantly to the hyperpolarization induced by HCO3-. These results along with the results of previous studies (Astion et al: J Gen Physiol 93:731, 1989) strongly suggest that the hyperpolarization induced by the addition of HCO3- is due to an electrogenic Na+/HCO3- cotransporter, which transports Na+, HCO3- (or its equivalent), and net negative charge across the glial membrane. To study the role of electrogenic Na+/HCO3- cotransport in the regulation of pHi in glial cells, we used intracellular double-barreled, pH-sensitive microelectrodes. At a bath pH of 7.5, the mean initial intracellular pH (pHi) was 7.32 (SD 0.03, n = 6) in HEPES-buffered Ringer's solution and 7.39 (SD 0.1, n = 6) in HCO3-/CO2 buffered solution. These values for pHi are more than 1.2 pH units alkaline to the pHi predicted from a passive distribution of protons; thus, these cells actively regulate pHi. Superfusion and withdrawal of 15 mmol/l NH4+ induced an acidification of 0.2 to 0.3 pH units, which recovered toward the original steady-state pHi.(ABSTRACT TRUNCATED AT 250 WORDS)

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An electrogenic Na+/HCO3- cotransport system was identified and characterized in freshly dissociated salamander Müller (glial) cells. Under voltage-clamp, these cells generated an outward current when external HCO3- concentration [( HCO3-]o) was raised. This current was Na(+)-dependent, Cl(-)-independent, and was blocked by the stilbenes 4,4'-diisothiocyanato-stilbene-2,2'-disulfonate (DIDS) and 4,4'-dinitrostilbene-2,2'-disulfonate (DNDS), and by harmaline, demonstrating that the current was generated by a Na+/HCO3- cotransport system. Substantially larger currents were evoked when [HCO3-]o was raised at the Müller cell endfoot as compared to other cell regions, indicating that cotransporter sites are localized preferentially to the endfoot. The reversal potential of the current, which varied as a function of HCO3- and Na+ transmembrane gradients, indicated that the cotransporter has a HCO3-:Na+ stoichiometry of 3:1.

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Single and double-barreled pH-sensitive electrodes were used to study intracellular pH (pHi) regulation in glial cells of Necturus optic nerve in the nominal absence of HCO3-/CO2. After the cells were acidified by the addition and withdrawal of NH4+, the pHi recovered toward the original steady-state pHi. The recovery from acidification was Na+-dependent and inhibited by 1 mM amiloride. These results suggest the existence in intact vertebrate glial cells of a Na+/H+ exchanger which functions in acid extrusion.

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