**Purpose**:
To detect visual field (VF) progression by analyzing spatial pattern changes.

**Methods**:
We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists.

**Results**:
In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (*P* < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60).

**Conclusions**:
The archetype method can inform clinicians of VF progression patterns.

^{1,2}is predominantly used for diagnosing glaucoma and monitoring its progression.

^{3–5}Given large VF test-retest variability, including short-term and long-term fluctuations,

^{6–8}detecting VF progression is a challenge for glaucoma management.

^{9}Numerous methods and criteria have been developed for determining VF progression in glaucoma.

^{4,5,10–15}In general, current VF progression detection methods can be divided into clinician-based evaluation and computer-based algorithms.

^{9,16,17}

^{18–20}Inevitably, such methods typically have low interrater agreement as reported due to their subjectivity.

^{18,19,21}

^{9,16,17}The Advanced Glaucoma Intervention Study (AGIS) criteria,

^{4}the Collaborative Initial Glaucoma Treatment Study (CIGTS) criteria,

^{5}and Guided Progression Analysis (GPA)

^{14}represent three well-known event-based methods. AGIS developed a dedicated defect score, calculated based on sector-weighted total deviation, and defined progression as three consecutive VFs with a defect score worsening of at least four units compared with baseline.

^{4,15}CIGTS developed a similar defect score, calculated based on sector-weighted total deviation probability, and defined VF progression as three consecutive VFs with a defect score worsening of three units or more compared with baseline.

^{5,15}Similarly, the GPA defines progression as two or three consecutive VFs with at least three identical worsening locations compared with baseline; the worsening at a single location is determined as the deterioration of pattern deviation values in the follow-ups exceeding outside of the 95% confidence interval for expected test-retest variability in stable glaucoma patients.

^{9,14,16,17}The proprietary GPA is derived from the Early Manifest Glaucoma Trial protocol.

^{22,23}

*P*value is used to define a progression detection threshold. Global measures including mean deviation (MD)

^{10,24–28}and VF index

^{12,16,17}are widely used for trend-based progression analysis due to their simplicity of implementation and interpretation. Alternatively, local measures using total deviation at individual test locations also were introduced to detect VF progression with linear regression, termed pointwise linear regression (PLR).

^{13}The most commonly used PLR criteria define VF progression as at least −1 dB per year change in total deviation for at least two or three locations with a

*P*value for the regression of less than 0.05 or 0.01.

^{29}Varying PLR methods have been developed to improve the detection performance.

^{29–31}Compared with global measures, location-based methods can potentially increase the sensitivity of VF progression detection because glaucomatous progression is highly location specific. This increase in sensitivity comes with a sacrifice of specificity.

^{32–34}

^{35–37}For instance, a technique called variational Bayesian independent component analysis has been applied,

^{35}which identifies major axes inside the VF data space. VF series of glaucoma patients were decomposed into the VF axis patterns and the coefficients of each VF axis pattern were regressed by follow-up time. Progression was defined as regression slopes of VF axis patterns exceeding the 95% confidence limits of pattern slopes in stable eyes. In most cases, the VF axis patterns do not resemble typical glaucomatous VF loss patterns,

^{35–37}limiting the utility of tracking progression.

^{38,39}which explicitly emphasizes distinctive features of the data. We previously applied this method to a clinical VF data set and mathematically identified 16 representative VF patterns (archetypes),

^{40}which resemble clinically recognizable patterns of VF loss.

^{41}Eleven of the 16 patterns bear typical features of retinal nerve fiber defects, which was confirmed by a clinical correlation study.

^{42}The glaucomatous VF archetypes were then successfully applied to improve the diagnostic accuracy of the Glaucoma Hemifield Test.

^{43}

^{24,44–48}

^{40}based on the total deviation values of more than 13,000 VFs. Any VF test can be represented as the summation of the 16 archetypes multiplied by their respective coefficients (the sum of the 16 coefficients is normalized to 1), as illustrated in Figure 1B. The clinical descriptions of each archetype are also denoted in Figure 1A. Archetype 1 represents a normal VF, and all other archetypes represent various VF defects, including a superior peripheral defect (archetype 2), superonasal and inferonasal steps (archetypes 3 and 5), a temporal wedge (archetype 4), a near total loss pattern (archetype 6), a central scotoma (archetype 7), superior and inferior altitudinal defects (archetypes 8 and 13), inferotemporal and inferonasal defects (archetypes 9 and 10), a concentric peripheral defect (archetype 11), temporal and nasal hemianopia (archetypes 12 and 15), as well as predominately superior and inferior paracentral defects (archetypes 14 and 16).

^{40}Linear regression was used to analyze the changes of the 16 archetype coefficients over time. Sixteen slopes {β

_{i}}

_{i=1:16}were extracted for progression detection. The limit of archetype slope variation due to long-term fluctuation was used as the slope threshold β

^{t}to detect progression. The slope threshold β

^{t}was calculated as the average of the absolute value of 2.5% and 97.5% percentiles over all 16-slope distributions in this dataset, which is approximately equivalent to a 5% significance level commonly used in the practice of statistics. The threshold β

^{t}was calculated as the absolute average of the lower and upper tails, because even if a VF series shows progression, some individual archetypes can be regressing, as all 16 archetype decomposition coefficients sum to 1, as constrained by the archetype algorithm.

_{1}) is less than or equal to −β

^{t}, that is, the normal VF archetype decreases substantially, or any of the slopes for other archetypes ({β

_{i}}

_{i=2:16}) is greater than or equal to β

^{t}(i.e., the abnormal archetypes increase substantially), the archetype method will indicate progression and generate the progressed archetype(s). Otherwise, the algorithm will label the VF series as nonprogressing and provide the most worsening archetypes for the clinician's information.

^{49}In each peripheral region, a VF defect is defined as a cluster of at least three adjacent test points conforming to nerve fiber layer topology with −5 dB or worse for each point. For the paracentral region, a VF defect is defined as at least two adjacent points with a sum of −15 dB or more. If the final VF in the series shows no VF defect in all regions, the status was no progression. Progression was defined in three ways: event-based progression is the presence of a VF defect in one or more regions in the final VF, reproduced on a prior VF but not seen in the baseline VF; trend-based progression occurs when a VF defect present in one or more regions on the final two VFs is worse than the first two tests (average pattern deviation [PD] value for all test points in the region worsened by −3 dB or more) or when the average MD values of the final two VFs was worse by −3 dB or more than the average of the first two VFs. A VF series can have both event-based (VF defect in a new region) and trend-based (VF defect worsening in another region) progression. Unconfirmed progression was defined as VF defect(s) present only on the final VF. Two of the three glaucoma specialists listed above reviewed each VF series together. Senior glaucoma specialists (LQS and LRP) reviewed all disagreement cases to reach unanimous decisions.

^{50}for comparing paired progression methods, Fleiss' kappa

^{51}for comparing more than two progression methods). The concordances between clinician evaluation and our archetype method as well as the four existing methods were assessed by Cohen's kappa coefficient. For the VFs used for clinician evaluation, hit rate and correct rejection rate were used to determine the detection accuracy of our archetype method in comparison with the four existing progression detection methods.

*P*< 0.001 for all) better MD, younger age, shorter follow-up time, and smaller number of VFs. The percentiles of 0%, 2.5%, 5%, 25%, 50%, 75%, 95%, 97.5%, and 100% for each archetype slope at different percentiles can be found in Supplementary Table S1. The slope threshold to assess progression was determined as 0.025 per year as the average of the absolute value of the 2.5% and 97.5% percentiles over the entire slope distribution (See Supplementary Table S1).

*P*< 0.001 for all three methods), and was in slight agreement with AGIS scoring (kappa: 0.12,

*P*< 0.001). The kappa coefficient among the four existing methods was 0.43 (

*P*< 0.001), which was in moderate agreement.

*P*< 0.001 for all,

*t*-test with bootstrapping) with the clinician evaluation. The agreement between the archetype method and clinician evaluation remained highest with kappa of 0.51.

*P*< 0.001,

*t*-test with bootstrapping) than AGIS (0.05), CIGTS (0.20), MD slope (0.26), and PoPLR (0.20). In comparison, the correct rejection rate of the archetype method (0.82) was significantly lower (

*P*< 0.001,

*t*-test bootstrapping) than AGIS (1.0), CIGTS (0.98), MD slope (0.92), and PoPLR (1.0). The archetype method outperformed all four existing methods in terms of overall accuracy measured by the mean of hit rate and correct rejection rate. The mean of hit rate and correct rejection rate of the archetype method (0.74 with unconfirmed progressions and 0.77 without unconfirmed progression) were significantly higher (

*P*< 0.001 for all, bootstrapping) than AGIS (0.52 and 0.52), CIGTS (0.58 and 0.59), MD slope (0.58 and 0.59), and PoPLR (0.58 and 0.60, respectively).

*P*= 0.002) than in moderate glaucoma (0.61). The hit rates in mild glaucoma by CIGTS and PoPLR (0.16 for both) were significantly (

*P*= 0.002 and 0.007) lower than in moderate glaucoma (0.22 for both). The correct rejection rate in mild glaucoma (0.82) by the archetype method was significantly higher (

*P*= 0.02) than in moderate glaucoma (0.76). The correct rejection rates in mild glaucoma by CIGTS and MD slope (0.98 and 0.91) were significantly (

*P*< 0.001 for both) lower than in moderate glaucoma (1.0 for both). There were no significant differences for hit rate and correct rejection rate for all other pairs not mentioned herein. Although the performance of the archetype method decreased with respect to glaucoma severity, the mean of hit rate and correct rejection rate by the archetype method was still significant higher (

*P*< 0.001) than all of the four existing methods (Supplementary Fig. S3).

^{40}and subsequently validated in a prior clinical correlation study,

^{42}progression was detected by regressing the archetype coefficients over time. Tested with 11,817 eyes from the method development cohort, the archetype method was in fair agreement with the existing methods of CIGTS (0.22), MD slope (0.37), and PoPLR (0.33). All

*P*values of kappa coefficients were <0.001. A group of 397 eyes separate from the 11,817 eyes were subsequently graded by glaucoma specialists based on clearly defined criteria, which differed from the method used by archetypal analysis. The clinician evaluation was used as the reference standard to evaluate the accuracy of our archetype method. The overall accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.77) significantly (

*P*< 0.001) outperformed the AGIS (0.52), CIGTS (0.59), MD slope (0.59), and PoPLR (0.60) methods. Interestingly, despite the different characteristics between training and validation datasets (Table), the archetype method still outperformed the four existing methods for progression detection.

^{15,36,52}We used clearly defined criteria for clinician assessment to increase reproducibility of our results in future studies. We did note that our archetype method had a lower correct rejection rate than the existing four methods. Admittedly, the performance of all computer algorithms including the archetype method is not ideal; nonetheless, the information of quantified VF pattern changes over time can be used by clinicians in their own decision-making process for assessing progression, especially because the archetype method will highlight regions that are worsening.

*P*< 0.001) decreased from 0.66 to 0.45, and the correct rejection rate significantly (

*P*< 0.001) increased from 0.82 to 0.93, when not using archetype 1 for progression detection. These results suggest that excluding archetype 1 for progression detection will improve correct rejection rate but at the expense of a reduced hit rate. As in the Ocular Hypertension Treatment Study (OHTS),

^{41}this is an analysis in which patients develop pathology other than glaucoma, including cataract, posterior capsular opacity after cataract surgery, macular disease, and higher visual pathway lesions. In OHTS, a manual approach of stratifying VF loss included glaucomatous and nonglaucomatous patterns. The method we propose should not be construed as a method to detect glaucomatous progression. Additional future work with the archetype method will be needed to better differentiate glaucomatous progression from nonglaucomatous VF deterioration.

^{15}whereas the specificities were comparable. In the work by Heijl and coworkers,

^{15}the hit rates of AGIS and CIGTS were 58% and 75%, respectively, compared with the 5% and 20% hit rate of AGIS and CIGTS in the clinical validation cohort of our study. A possible reason might be the considerably smaller number of VF follow-ups in our study: mean VF number for each eye of 6 compared with 25 and 22 for the progressing and nonprogressing groups, respectively, in the study by Heijl and coworkers.

^{15}

^{35–37,53}It has been reported that this type of artificial intelligence approach was able to detect progression accurately.

^{36,37,53}Compared with previous approaches for detecting progression of patterns by artificial intelligence methods, our archetype method provides quantitative progression patterns of VF loss; these patterns resemble clinically recognizable VF defects in glaucoma and were clinically validated,

^{42}thus are more interpretable and assessable by clinicians. It is worth noting that our reference standard of clinician's progression assessment was determined purely based on VF data, whereas in the previous work of detecting progression by pattern analysis, the reference standard of progression was determined by inspection of serial stereoscopic optic nerve images.

^{36}The difference in reference standard might explain their higher overall accuracy of progression detection, especially for the PoPLR method, also performed better in their dataset than in our validation dataset.

^{54,55}In future studies, sigmoid or other nonlinear regression methods could be used to model the pattern-changing rates over time.

^{54,55}Third, the glaucoma progression analysis (GPA) method for progression detection was not included in our work for comparison purpose, because this method is proprietary and we were unable to obtain GPA datasets for this study. Fourth, the reference standard in this work was established based on the clinical evaluations by three glaucoma specialists without consulting any other clinical data. In future studies, larger numbers and different groups of clinicians might evaluate our archetype method, especially in context of other clinical and structural optic nerve data. Also, VF series repeated within a short time range can be used as the reference for nonprogression, but was not available for this study.

^{56}Fifth, for each VF series, the clinician assessment in this study was performed only once, whereas in clinical practice the clinician assessment is performed repeatedly over the course of follow-ups. In our future study, cumulative performance will be evaluated for our archetype method compared with other existing methods of progression detection.

**M. Wang**, Adaptive Sensory Technology (R) P;

**L.Q. Shen**, Genentech (C), Topcon (C), P;

**L.R. Pasquale**, Visulytix (C), Bausch + Lomb, Inc., (C), Verily (C), Eyenovia (C), P;

**P. Petrakos**, None;

**S. Formica**, None;

**M.V. Boland**, None;

**S.R. Wellik**, None;

**C.G. De Moraes**, None;

**J.S. Myers**, None;

**O. Saeedi**, Heidelberg Engineering (C), Vasoptic Medical, Inc. (C);

**H. Wang**, None;

**N. Baniasadi**, Adaptive Sensory Technology (R);

**D. Li**, Adaptive Sensory Technology (R);

**J. Tichelaar**, None;

**P.J. Bex**, P;

**T. Elze**, Adaptive Sensory Technology (R), P

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