The subject invention relates to the detection of statistically significant changes in the topography of a structure within the eye. Of particular interest are changes in the eye determined by optical measurements of the retinal nerve fiber layer (RNFL). More specifically, an approach is described where the thickness of the RNFL is evaluated using at least two different analysis techniques in order to improve diagnostic accuracy. Improved methods for displaying the results are also disclosed.
Accurate assessment of RNFL thickness makes early detection and better management of glaucoma possible. Traditionally, glaucoma is monitored by testing for loss of vision. By the time vision loss is detected, a significant amount of nerve fiber may have already been compromised. In contrast, using recently developed optical instruments, structural damage to the RNFL can be detected before field vision loss is detectable. Early detection enables early treatment and improved outcomes. RNFL damage is highly correlated with a structural diagnosis of glaucoma.
Several modem devices can provide a measure of RNFL thickness. The assignee herein markets the GDx™ scanning laser polarimeter, which measures the retardance of the RNFL using a polarimetry technique. The measured retardance is proportional to the RNFL thickness. The assignee also markets the Stratus OCT™ and Cirrus™ HD-OCT retinal imagers which use Optical Coherence Tomography (OCT) to measure the RNFL thickness.
While these devices have provided clinicians with improved tools for detecting glaucoma, there is a continuing need for sensitive and reliable detection of glaucomatous progression. Glaucoma progression happens slowly. Early detection of degradation in the RNFL or visual function enables earlier and more effective medical intervention, improving visual function outcomes. The subject disclosure is directed to a number of improvements in data analysis algorithms, integration of the analyses, and display techniques which facilitate the early detection of disease progression. These improvements can be implemented using any instrument which obtains spatial measurements of structures within the eye or functions of the eye that can then be analyzed in accordance with the subject invention.
The present invention is defined by the claims and nothing in this section should be taken as a limitation on those claims. Advantageously, embodiments of the present invention overcome the above-described problems in the art and provide analysis techniques and displays improving diagnostic accuracy.
In one aspect of the subject invention, tissue data are obtained over at least two visits. The data are evaluated to determine if there has been a statistically significant change in a characteristic of the tissue between the visits. More than one type of analyses are used in combination to improve the accuracy of the evaluation.
In another aspect of the subject invention, tissue data are obtained over at least three visits. Tissue data may be topography data, it may be tissue thickness data, or it may be data descriptive of other tissue characteristics.
In another aspect of the subject invention, tissue changes are parameterized into global, regional and local measures for a multi-modal change detection method.
In another aspect of the subject invention, the tissue data are RNFL measurement data. RNFL measurement data obtained over at least two visits are evaluated using more than one type of analyses to determine if there has been a statistically significant loss in RNFL thickness.
In another aspect of the subject invention, techniques are developed to improve accuracy of RNFL change detection, including detecting/excluding blood vessel and ONH regions, employing dual baselines, and confirming RNFL loss with additional follow-up visit.
In another aspect of the subject invention, when multiple scans per visit are available for analysis, individual-based test-retest variability is applied to identify patient-specific statistically significant RNFL loss.
In another aspect of the subject invention, when individual-based test-retest variability cannot be assessed due to lack of repeated measurements per visit, population-based test-retest variability is applied to identify statistically significant RNFL loss.
In another aspect of the subject invention, certain display techniques have been developed to convey to the clinician the most relevant aspects of the analysis. In one aspect, the display color codes regions of concern using fundus image overlays. In another aspect, the display color codes significant change in the TSNIT plots based on regional analysis. In another aspect, trend charts display the statistical significance of the progression of the disease based on global analysis and the rate of RNFL loss to facilitate assessment of clinical significance of the detected progression.
The analysis of the change over time is very important in determining disease progression. The detection of RNFL change is very important in determining glaucomatous progression. A reliable change detection method and a comprehensive and easy-to-understand report are therefore extremely desirable, for both the clinicians and the patients. The subject invention meets a long-felt and unsolved clinical need.
It should be understood that the embodiments, examples and descriptions have been chosen and described in order to illustrate the principles of the invention and its practical applications and not as a definition of the invention. Modifications and variations of the invention will be apparent to those skilled in the art. The scope of the invention is defined by the claims, which includes known equivalents and unforeseeable equivalents at the time of filing of this application. While the description herein relates primarily to thickness and topographic measurements of the retina, the subject invention can be applied to other measurements tissue characteristics structures within the eye. While the tissue characteristics described herein are primarily acquired and stored by a GDx™ scanning laser polarimeter, these tissue characteristics could alternatively have been acquired by any of various alternative devices, including, but not limited to, the Stratus OCT® ophthalmic imager, Visante® OCT ophthalmic imager, Cirrus™ HD-OCT ophthalmic imager, or various other devices. The embodiments, examples and descriptions chosen to describe and illustrate the principles of the invention and its practical applications will, for the most part, be based on application of the invention to polarimetric RNFL measurements acquired with the GDx™ scanning laser polarimeter, in particular the GDx VCC and its successors. Modifications and variations of the invention will be apparent to those skilled in the art.
Change in a tissue characteristic is statistically significant when the magnitude of the change exceeds the test-retest measurement variability [8]. Relatively small changes in retinal thickness extending over a large area are clinically relevant because they may provide an early indication of glaucoma. Even a small change in thickness, consistent over a large area, is readily detectable by a statistically reproducible global parameter such as an average thickness derived from a large number of independent measurements. Statistically significant changes may be either global or regional in nature and are differentiated by the scope of their support. On the other hand, large changes in retinal thickness, even if limited to a relatively small area, are also clinically relevant. Analysis of localized parameters, which inherently exhibit higher measurement variability, nominally detects large changes over small regions. Therefore, in accordance with the subject invention, it is desirable to analyze the data using more than one statistic in order to capture global, regional, and/or local changes that are essential to clinical interpretation of changes in a tissue characteristic. In particular, analyses of more than one statistic measuring global, regional and/or local change in tissue thickness is of immense clinical value in interpreting and predicting glaucomatous progression.
Summary parameters, such as average TSNIT, or parameters averaged over a global region of interest, such as TSNIT averaged over superior or inferior quadrants, exemplify parameters used in global change detection. Global change detection looks at a measure over a broad region and identifies change over the region as a whole. Global change detection is used to identify relatively small levels of change over the entire measurement area (or a substantial portion of the entire measurement area). Statistically, global detection can detect smaller changes than local or regional detection.
Regional change detection identifies change over regions smaller than the entire field of view, such as over clusters of pixels. Sectional measurements about the optic nerve head (ONH), such as the TSNIT plot, exemplify parameters used in regional change detection. Statistically, regional detection is used to detect smaller changes in depth than local detection but requires larger changes than needed by global detection. Regional change detection provides sensitivity and selectivity with respect to changes in size and changes over area where neither global nor local detection are well suited.
Local parameters, such as pixel-by-pixel measurements of RNFL thickness, exemplify parameters used in local change detection. Localized change detection detects changes over measurement points or pixels. Statistically, local detection requires larger changes for detection than regional or global detection. Nominally, local change detection compares RNFL image measurements about the optical nerve head (ONH). Local detection is associated with early indicator of glaucomatous pathology such as wedge defects. Frequently a wedge is a segment of an annular ring, however, the term may also apply trapezoidal or even nearly rectangular shape. The term “wedge” generally refers to a region commonly wider further from the ONH and narrower nearer the ONH, but generally applies to other regions of limited scope.
In one instance, global, regional and local change detection are performed through an event-based and population-based algorithm (Change-From-Baseline (CFB)) [8]. In another instance, global, regional and local change detection are performed through a trend-based and individual-based algorithm (Statistical non-Parametric Mapping (SnPM) or Statistical Image Mapping (SIM)) [8]. CFB detects change based on a triggering event of RNFL reduction in follow-up visits. SIM analyzes the trend of the RNFL measurements and detects statistically significant trends of RNFL loss. These algorithms were used elsewhere prior to this invention; however, novel and non-obvious changes have been made to improve the performance of the algorithms for change detection. In particular, the combination of multi-modal tests is novel and central to one aspect of our invention.
Since it is desirable to perform change detection across different instruments, both the CFB and SIM have been modified to handle change detection on inter-instrument measurements while retaining specificity.
In order to improve the accuracy of change detection, areas obscured by blood vessel as well as areas within the ONH can be excluded in Change Analysis.
Use of more than one baseline visit can provide a more robust baseline reference for comparison with the follow-up visits and reduces the likelihood of false alarm detection for CFB based analyses.
An inter-visit confirmation approach can be employed to reduce the likelihood of false alarm detection. Such an inter-visit confirmation approach requires changes detected the first time in a parameter to be confirmed in a subsequent visit for the same parameter.
In one aspect of the invention, a comprehensive change detection report is designed to display and summarize the multi-modal RNFL change detection results. The detection report communicates the multi-modal change detection results in a simple and clinically meaningful way. This report is particularly useful for the doctor or examining practitioner, but can also be a valuable tool for communicating with the patient or care provider. The report provides a summary of the multi-model change analysis. One such report contains detailed information of the quality of the measurement data, display images (local analysis) and TSNIT plot (regional analysis) with areas of statistically significant change highlighted in colors, provides trend charts of the summary parameters (global analysis) with statistically significant change highlighted in colors. Importantly, this report provides an assessment of the rate of the RNFL loss.
The multi-modal change detection of RNFL measurements is important for monitoring and detecting progression of glaucoma. In glaucoma progression detection, global, regional and local changes each provide diagnostically useful information for the treatment and monitoring of the disease. Each of these detection modes can be clinically informative individually, but they can also be synergized to improve sensitivity of overall change detection. A comprehensive change detection report provides a vehicle to synergize the information of said detections.
The particular algorithm selection is not an essential part of the subject invention. Alternative algorithm selections may achieve similar performance. For example, SIM may be employed in all three (3) modes in Extended Analysis and CFB may be employed for all three (3) modes in Extended Change Analysis as well. As will be understood by those versed in the art, other algorithms distinguishing or identifying change may be used as well.
The CFB method compares the difference between follow-up visits and the baseline visits to a measure of the reproducibility. In Fast Analysis, the measure of reproducibility is set to a fixed value. On the other hand, in Extended Analysis, the measure of reproducibility is determined based on the repeated measurements of the test eye.
The SIM method is based on the assumption that in the absence of change, a measure of change should be insensitive to random permutations of the measurements. If change is present, the observed order of measurements yields a value that is more extreme than the values in most of the permutations. In one embodiment, the measure of change is defined as the ratio of the slope (measurement value versus time) of linear regression and its standard error. Alternatively, the measure of change may be any measure describing the trend information of the data.
A clear and accurate message is useful at the conclusion of the multi-modal analysis. In one embodiment, if a change is detected for the first time in a parameter, it is labeled as “Possible” change; if such change is confirmed in a consecutive visit, it is labeled as “Likely” change. The particular naming is not an essential part of the subject invention. Alternative clinically useful terminology may achieve similar benefit. For example, a change detected for the first time can be labeled as “Change” and change confirmed in a consecutive visit can be labeled as “Confirmed Change”. For consistency, “Possible” change and “Likely” change will be used hereinafter. (See
The integration of the multi-modal analysis is such that if “Likely” change is detected in any one of the multi-modal measures, “Likely” RNFL change is reported for the test eye; if only “Possible” change is detected in one or more measures, “Possible” RNFL change is reported for the test eye; if neither “Likely” or “Possible” change is detected in any of the measures, “No change detected” is reported for the test eye. Alternative integration logic may be applied. For example, when three or more multi-modal measuring techniques are used and a high priority is set for eliminating false alarms, the report may require that two measuring techniques agree before a “Possible” or “Likely” change is declared. Alternatively, if the sensitivity of the various techniques are different or vary, a probabilistic result may be reported.
An analysis change report summarizes the results of the multi-modal analysis and integration.
The statistical analyses employed in the multi-modal change detection are based on the CFB-based algorithm and the SIM-based algorithm. The CFB algorithm has been used in opthalmology to detect topographic changes on and around the ONH (such as the approach described by Chauhan and adopted by the optical instrument manufacturer Heidelberg in their Heidelberg Retina Tomograph (HRT) imaging device). The SIM algorithm has been used in the field of radiology and opthalmology to detect change (such as the approach described by Patterson). However, separate and significant modifications to these prior art methods (discussed-below in the following five (5) paragraphs) are required to improve sensitivity and specificity of multi-modal change detection developed herein.
Topographic Change Analysis (TCA) for topographic measurement of the optic nerve was published in 2000 by Chauhan et al. The CFB approach herein is similar to the TCA approach in that they are both event detection based on change from baseline. Four key differences between the Chauhan TCA and our CFB follow.
1) CFB is based on two (2) baselines and TCA is based on one (1) single baseline. CFB two-baseline approach is based on the important observation that inter-visit test-retest variability plays a key role in the measurement variability assessment, in addition to the intra-visit test-retest variability (the proposed dual baselines approach helps to improve the progression detection specificity in the presence of inter-visit variability).
2) The CFB approach herein has been extended from individual-based change analysis to include population based change analysis so that longitudinal data series with only one (I) measurement per visit can also be analyzed with this approach. This extends the approach to cases where individual test-retest variability is not available.
3) CFB developed herein makes clear distinction between intra- and inter-instrument measurements and applies the appropriate test-retest variability accordingly.
4) Finally, for the multi-modal analysis to detect both diffuse and local loss, the cluster size threshold for different modes are selected based on a preferred clinically meaningful size and then the threshold for the significance level is selected accordingly to achieve the desired specificity. This distinguishes the method from the prior art references [1-3] which first selected the threshold for the significance level and then the detection size, which usually rendered detection size immaterial to clinical use. The relationship between the significance level threshold and the detection size threshold are investigated in Vermeer et al [6].
SIM was introduced into opthalmology for topographic image change analysis by Patterson et al in May 2005. The technique was well known in the field of radiology for a much longer time. Our implementation of SIM has significantly deviated from the initial approach reported by Patterson et al. The key differences include: 1) in order to detect change in TSNIT plot with the SIM approach, the algorithm is modified to account for the spatial characteristics of test-retest variability; and 2) SIM developed herein makes clear distinction between intra- and inter-instrument measurements and applies different regression model accordingly.
The SIM method described in the referenced Patterson article employs a three-step approach to find an area showing change. In the first step, each data point is evaluated individually and converted to a probability score (p-value). The second step thresholds these points, and determines the maximum size of the resulting clusters. By repeating this for different permutations, each cluster size would be associated with a probability score and the area statistic can then be determined. The third step determines the area statistic of the observed order of measurements and compared to those obtained in step two. A change would be detected if the observed area statistic (from the observed order of the measurements) were smaller than a set percentage of those generated in the different permutations from step two. However, this approach only works well if the noise in the data points is not strongly correlated. For instance, if the noise is spatially fully correlated, either all measurement points, or none at all, would show change exceeding the selected p-value threshold while converting each data point into a probability score in step one. This would result in large area statistics for many permutations and would render detection of small area of loss impossible because a small area of loss has a small area statistic.
One instance of the subject invention solves said problem by scaling the p-values of the data points in step one to incorporate data from other spatial location(s) of interest, such as neighboring pixels in a 2-D image or neighboring points in a 1-D data series. Information from such data points would be used to determine the scaling for each p-value. This scaling helps reduce the impact of the spatially correlated noise.
The p-values may be scaled in various ways. The scaling should be such that the most extreme change in the data points corresponds to the most extreme p-values (e.g. p=0 or p=1) and neutral values (e.g. p=0.5) should remain neutral or nearly neutral. For a linear scaling, the mathematical relationship (for each point) between the unscaled p-values and the scaled p-values is:
P
scalcd=(Punscaled−0.5)·w+0.5
In this equation, w specifies the scaling factor with values between 0 and 1. If w=0, all values are transformed to neutral p-values (p=0.5). For w=1, the scaled p-values will exactly match the unscaled ones. The scaling factor w incorporates information of the entire data set to be analyzed. For example, in the regional analysis, the entire TSNIT plots would be used to provide scaling for each individual TSNIT plot.
The relative slope of each point may be used to determine the scaling factor. Alternatively, the ratio between the slope and the standard error of the measurements can also be used.
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In some instances, said masks may be applied to regional and local analysis and not to global analysis. In other instances, said masks can be applied to all type of multi-modal analysis.
Masking blood vessels may leave objectionable holes in the data field that are problematic or inconvenient for later analysis. For this reason, data points on either side of a blood vessel may be connected for analysis. For example, in
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(1) obtaining input measurements and performing registration,
(2) selecting a Change Analysis strategy (Fast Analysis or Extended Analysis),
(3) calculating test-statistics for analysis,
(4) performing confirmation of test-statistics,
(5) flagging change confirmed with previous visit, and
(6) displaying said statistically significant change.
The first step in CFB is to obtain and register measurements 21. The first decision in CFB is to select the appropriate Change Analysis strategy 22, depending on the number of measurements per visit. Fast Analysis can be selected for any number of measurements per visit. Alternatively, Fast Analysis is performed when there is one (1) or two (2) measurement per visit. In another aspect of the subject invention, two (2) baseline visits and a minimum of one (1) follow-up visit are required for CFB analysis. The next step is to calculate the test-statistics for the analysis 24. The test-statistic (t) between a follow-up visit and a baseline visit is defined as the difference between the measurements. The next step is performing confirmation of test-statistics 25. Thresholds specific to image mode, number of confirmation test and inherent test-retest variability (intra- or inter-instrument) are used to determine statistically significant change. Negative change is detected when t is less than such thresholds; positive change is detected when t is greater than such thresholds. When there are three (3) visits, two (2) possible test-statistics are calculated (t3-1—test-statistics between the follow-up visit and the first baseline visit; and t3-2—test-statistics between the follow-up visit and the second baseline visit) and such test-statistics are combined on a 2-out-of-2 principle to confirm the change(s) detected in each test-statistic. Similarly, when there are four (4) or more visits, four (4) possible test-statistics are calculated (t3-1—between first follow-up and first baseline; t3-2—between first follow-up and second baseline; t4-1—between second follow-up and first baseline; and t4-2—between second follow-up and second baseline) and such test-statistics are combined to confirm the change(s) detected in each test-statistics. In one embodiment, for four (4) or more visits, the confirmation is based on a 3-out-of-4 (75%) principle. Alternatively, the confirmation can be 2-out-of-4 (50%) principle to enhance sensitivity. Similarly, the confirmation can be 4-out-of-4 (100%) principle to enhance specificity. This utilization of the double-baseline visits is different from the prior art method [4] where the double-baseline visits are averaged to create one (1) single baseline for comparison. The next step is confirming change with previous visit 26. The intra-visit change(s) 25 is/are further confirmed with intra-visit change(s) 25 from previous visit. For instance, if a change is detected in one visit, but the same change is not confirmed in subsequent visit, then no change is detected. On the other hand, if a change is detected in one visit and again confirmed in subsequent visit, it is likely that change has occurred. Such inter-visit confirmation combines change(s) from sequential visit(s) and helps increase the accuracy of detection. The last step of the CFB scheme is displaying said intra-visit and inter-visit confirmed change(s) 27.
In the
In one embodiment, sixty-four (64) TSNIT plot points are used in CFB regional change detection. Alternatively, other numbers of representative regional measures can be used. CFB is performed on each individual TSNIT plot point as described above. Flagged point(s) on either side of the blood vessel point(s) is/are connected (
CFB may use a region of interest on a 2D image measurement as the basis for local change detection. The region of interest can be of any meaningful size, but is generally larger than 5% of the total, with either individual pixels or super-pixels as the basis unit. A measurement point coinciding with blood vessel area is not used for calculation. CFB is performed on each individual measurement point as described above. As shown in
Extended Analysis provides change detection for longitudinal data series with two (2) or more visits. In the
SIM is the algorithm of choice for the Extended Analysis process of
The first step in SIM is to obtain all measurements from each visit for all visits and perform image registration 61. The next step is creating permutations 62. Adequate number of unique and distinct permutations is performed to obtain a good distribution of trend information. The next step is performing regression analysis 63. In one embodiment, linear regression is used for the regression analysis. Alternatively, higher order of regression model can also be used. The next step is calculating test-statistic, p-values and cluster 64. In one embodiment, test-statistic t is defined as the slope of the linear regression model divided by the standard error of the slope. Alternatively, other relative measure of the trend information can be used as the test-statistic for SIM. For inter-instrument data, an offset is added to the regression model to preserve a continuous slope across all visits. A distribution of said test-statistics is obtained and is converted into p-values for statistical comparison. The next step is detecting change exceeding a threshold 65. The test-statistic from the observed order of measurements is then compared to the populations of test-statistics obtained above from the permutations. A change is detected when the test-statistic of the observed order exceeds a desired threshold. The next step is confirming detected change with previous visit 66. Change detected from one (1) visit is confirmed with change in the subsequent visit. For instance, if a change is detected in one visit, but the same change is not confirmed in a subsequent visit, then no change is detected. On the other hand, if a change is detected in one visit and again confirmed in subsequent visit, it is likely that change has occurred. This confirmation approach helps increase the accuracy of detection. The last step of the SIM scheme is displaying the confirmed change(s) in an integrated report 67.
In the
In one embodiment, sixty-four (64) TSNIT plot points are used in SIM regional change detection. Alternatively, other numbers of representative regional measures can be used. SIM is performed on each individual TSNIT plot point as described above. In one instance, the test-statistic used is defined as the slope of the regression model divided by a smoothed version of the standard error to reduce noise. The p-values converted from the test-statistics may be scaled by a weight factor using trend information of all TSNIT plot points (discussed supra). As shown in
CFB is performed on each individual measurement point as described in the CFB section. The same CFB approach for local analysis discussed in the Fast Analysis section can be applied in the local analysis in the Extended Analysis. CFB may use a region of interest on a 2D image measurement as the basis for local change detection; the region of interest can be of any meaningful size and is based on either individual pixels or super-pixels; and measurement points coinciding with blood vessel area is not used for change analysis. The Extended Analysis CFB local analysis different from the Fast Analysis CFB local analysis in two (2) ways. First, Extended Analysis CFB uses mean images for comparison. Second, test-statistic in Extended Analysis CFB is defined as the mean difference between the follow-up visit and a baseline visit divided by the square root of the pooled intra-visit variance of all visits up to the visit of interest (23 and 24 in
Similarly, using said two differences, Extended Analysis CFB can be applied to other modes of multi-modal analysis, such as global and regional analysis. Alternatively, the same Extended Analysis SIM can be applied to local analysis on image measurements with the same scaled p-value and cluster analysis approaches.
When change is detected in a longitudinal data series, it is important to estimate the rate of change to facilitate assessment of clinical significance. In one embodiment, the rate of change is provided for global analysis (for summary parameters) and implementation is similar for both Fast Analysis and Extended Analysis. While the implementation may only covers global analysis, similar trend analysis can be implemented for regional and local analysis and the trend information can be presented in a table, a plot, or an image format. The following description focuses on the implementation for global analysis.
In one aspect of the subject invention, the output of the trend analysis is based on linear regression; the slope, 95% confidence intervals of the slope, and the p-value significance of the slope are reported. Alternatively, other trend information can also be displayed, such as confidence intervals of the slope at different significance level, prediction intervals of the slope, relative trend information, so forth and so on. For inter-instrument data series, the linear regression model includes an offset parameter between measurements from different instruments to accommodate instrument bias. Alternatively, nonlinear regression may be applied if warranted by data quality and expected model behavior.
In another aspect of the subject invention, positive trends are differentiated from negative trends and a statistically significant trend is plotted along with the 95% prediction or 95% confidence intervals, or other desirable significance levels. A clear display of a trend line and prediction intervals for inter-instrument data series can reveal instrument induced measurement variation and other inter-instrument data characteristics.
In another aspect of the subject invention, dual trend analysis is implemented to facilitate the comparison of a trend before and after clinical intervention.
Inter-visit confirmation improves change analysis specificity. Implementation of the inter-visit confirmation is similar for both Fast Analysis and Extended Analysis.
Change detection differentiates between a change that is first detected (“Possible” change) and a change that is confirmed with a consecutive visit (“Likely” change) within each test parameter.
For summary parameters in the global analysis, if negative change is detected one time in any parameter, such change is labeled as “Possible loss”. If negative change is detected in two consecutive visits for the same parameter, such change is labeled as “Likely loss”. If positive change is detected at any time in a parameter, such change is labeled as “Possible increase”.
For the TSNIT plot in regional analysis, the same rules of inter-visit confirmation for the parameters apply. Additionally, for the TSNIT plot to be labeled as “Likely loss”, the cluster size of the confirmed change should exceed a predetermined meaningful cluster threshold on two consecutive visits. Confirmed change compares clusters in the same location. Cluster thresholds can be the same threshold used in the CFB regional analysis of three (3) TSNIT plot points cluster. Alternatively, other desired cluster size of interest can be used.
The same rules for the inter-visit confirmation for the parameters apply in the local analysis of an RNFL image. Additionally, for the RNFL image to be labeled as “Likely loss”, the cluster size of confirmed change should exceed a predetermined meaningful cluster threshold in two consecutive visits in the same locations. Such threshold can be the same threshold as in the CFB local analysis, one-hundred-fifty (150) pixels cluster. Alternatively, other desired cluster size of interest can be used.
The exact labeling of the detected change is not important to the subject matter of the invention. Other meaningful labels can also be used to signify change detected at one time and change detected and confirmed with subsequent visits.
Integration of the multi-modal analysis is provided through a change analysis summary. Implementation is similar for both Fast Analysis and Extended Analysis. Such a summary of change analysis integrates the detection results of each of the three (3) modalities.
If “Likely loss” is detected in at least one (1) of the three (3) modalities (global, regional, and local), the summary of the change analysis would conclude “Likely loss” is detected. If no “Likely loss” is flagged and “Possible loss” is flagged in at least one (1) of the three (3) modalities, the summary of the change analysis would conclude “Possible loss”. If neither “Likely loss” nor “Possible loss” is flagged, the summary of the change analysis would conclude “no loss detected”. Alternatively, an integrative conclusion of such change analysis can be combined from a weighted sum of each modality. Other integration techniques are possible, especially when additional modalities are present. Depending on decision criteria for sensitivity and specificity additional tools known to those versed in the art of decision theory can be applied. If a sufficient model is available, fuzzy logic reasoning may be applied. Alternative rule based decisions can be used to alter control false positives or false negatives.
The integration stage is an important step to achieve sensitivity to different shapes (diffuse, focal, or other morphological shapes) and different depth of loss with the multi-modal change detection. The design philosophy is that different modes in the change detection are tuned to be more sensitive to different shapes and depth of change, and therefore, it is not necessary for a change to be detected in more than one mode for the eye to be flagged as changed. Alternative design philosophies may combine different modalities with different sensitivities and may then require an integration stage requiring multi-modal detection to flag change.
A comprehensive change detection report is designed to display and summarize the multi-modal RNFL change detection results. The detection report is instrumental in communicating the multi-modal change detection results to the doctor and the patient in a simple and clinically meaningful way.
One format for the report is shown in
The report provides a summary of the multi-modal change analysis, offers detailed information of the quality of the measurement data, displays images (local analysis) and TSNIT plot (regional analysis) with areas of statistically significant change highlighted in colors, furnishes trend charts of the summary parameters (global analysis) with statistically significant change highlighted in colors, and importantly, presents assessment of the rate of the RNFL loss.
As shown in
The Life Milestones may be specific ages, dates, or actuarial estimates. Actuarial estimates such as 50th percentile life expectancy or 95th percentile life expectancy or any other statistically stratified or not statistically stratified life expectancy estimate, e.g. statistical life expectancy percentile estimates based upon the specific medical status of the particular patient under consideration, perhaps based upon blood analysis or genetics or other medical index. Their purpose is to highlight the expected impairment that a predicted trend predicts at a particular Life Milestone. This display highlights the risk versus reward attributes for one or more treatments (or lack of treatment). The trend need not be a linear prediction, but may be a higher order polynomial or other modeled trend.
It should be understood that the embodiments, examples and descriptions have been selected and described in order to illustrate the principles of the invention and its practical applications and not as a definition of the invention. The subject invention can be applied to other topographical structures imaged using other imaging modalities. Such structures and modalities include, but are not limited to: RNFL thickness maps or optic nerve head (ONH) topography acquired using an Optical Coherence Tomography (OCT) device, ONH topography acquired using a fundus imager such as a confocal scanning laser opthalmoscope, or corneal topography measured using OCT or ultrasound. Modifications and variations of the invention will be apparent to those skilled in the art. The scope of the invention is defined by the claims, which includes known equivalents and unforeseeable equivalents at the time of filing of this application.
The following references are incorporated herein by reference:
This application claims the benefit of the filing date under 35 U.S.C.§ 119(e) of Provisional U.S. Patent Application Ser. No. 60/936,066, filed on Jun. 18, 2007 and Provisional U.S. Patent Application Ser. No. 60/962,911, filed on Aug. 1, 2007, which are hereby incorporated by reference in their entirety.
Number | Date | Country | |
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60936066 | Jun 2007 | US | |
60962911 | Aug 2007 | US |