Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.

PURPOSE: To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algorithm. DESIGN: Prospective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices. PARTICIPANTS: A total of 1127 eyes of 651 Age-Related Eye Disease Study 2 10-year Follow-On Study (AREDS2-10Y) participants with SD-OCT scans graded by reading center graders (as the ground truth). METHODS: The AREDS2-10Y investigators graded each SD-OCT scan for the presence/absence of intraretinal and subretinal fluid. Separately, the same scans were graded by the NOA. MAIN OUTCOME MEASURES: Accuracy (primary), sensitivity, specificity, precision, and F1-score. RESULTS: Of the 1127 eyes, retinal fluid was present in 32.8%. For detecting retinal fluid, the investigators had an accuracy of 0.805 (95% confidence interval [CI], 0.780-0.828), a sensitivity of 0.468 (95% CI, 0.416-0.520), a specificity of 0.970 (95% CI, 0.955-0.981). The NOA metrics were 0.851 (95% CI, 0.829-0.871), 0.822 (95% CI, 0.779-0.859), 0.865 (95% CI, 0.839-0.889), respectively. For detecting intraretinal fluid, the investigator metrics were 0.815 (95% CI, 0.792-0.837), 0.403 (95% CI, 0.349-0.459), and 0.978 (95% CI, 0.966-0.987); the NOA metrics were 0.877 (95% CI, 0.857-0.896), 0.763 (95% CI, 0.713-0.808), and 0.922 (95% CI, 0.902-0.940), respectively. For detecting subretinal fluid, the investigator metrics were 0.946 (95% CI, 0.931-0.958), 0.583 (95% CI, 0.471-0.690), and 0.973 (95% CI, 0.962-0.982); the NOA metrics were 0.863 (95% CI, 0.842-0.882), 0.940 (95% CI, 0.867-0.980), and 0.857 (95% CI, 0.835-0.877), respectively. CONCLUSIONS: In this large and challenging sample of SD-OCT scans obtained with 2 common devices, retinal specialists had imperfect accuracy and low sensitivity in detecting retinal fluid. This was particularly true for intraretinal fluid and difficult cases (with lower fluid volumes appearing on fewer B-scans). Artificial intelligence-based detection achieved a higher level of accuracy. This software tool could assist physicians in detecting retinal fluid, which is important for diagnostic, re-treatment, and prognostic tasks.

Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration.

PURPOSE: The objective of the study was to evaluate the accuracy of the Notal OCT Analyzer (NOA) versus that of a retina specialist (RS) in the automated detection of fluid on optical coherence tomography (OCT). DESIGN: A study of the performance of the NOA compared with the results from 3 RSs. PARTICIPANTS: A selection of 155 anonymized OCT scans (Zeiss Cirrus; Carl Zeiss Meditec, Dublin, CA) from an image repository at a single tertiary referral retina center (Belfast Health and Social Care Trust, Belfast, United Kingdom) after approval from the local data guardian of the clinical site. METHODS: One hundred fifty-five OCT cube scans were stripped of all clinical identifiers and exported. The NOA and 3 independent RSs analyzed all 128 B-scans of each cube scan for the presence of intraretinal fluid, subretinal fluid, and sub-retinal pigment epithelium fluid. The NOA also ranked individual B-scans of each volume scan for likelihood of CNV activity, which was subjected to a second grading session by the 3 RSs. MAIN OUTCOME MEASURES: The NOA's sensitivity and specificity versus the RS grading and the NOA's performance in ranking B-scans for activity. RESULTS: One hundred forty-two cube scans met the inclusion criteria for the primary analysis. On testing the RS grading versus the NOA, the accuracy was 91% (95% confidence interval [CI], ±7%), sensitivity was 92% (95% CI, ±6%), and specificity was 91% (95% CI, ±6%), meeting the primary outcome. The graders' accuracy when compared with the majority of the other graders (including a fourth grader) was 93%. On average, the 3 graders could identify fluid in 95% of scans by just reviewing a single cross-section with the highest NOA score and 99.5% of scans with fluid by viewing the top 3 cross-sections. CONCLUSIONS: Concordance between the NOA and the RS determination of lesion activity was extremely high. The level of discrepancy between the RS and the NOA results was similar to the NOA's mismatches. Our results show that automated delineation of the retinal contours combined with interpretation of disease activity is feasible and has the potential to become a powerful tool in terms of its clinical applications.

Impact of macular fluid volume fluctuations on visual acuity during anti-VEGF therapy in eyes with nAMD.

OBJECTIVES: To study the effect of repeated retinal thickness fluctuations during the anti-VEGF therapy maintenance phase in neovascular age-related macular degeneration (nAMD). METHODS: Data were extracted from electronic medical records of 381 nAMD patients, aged ≥50 years; baseline VA ≥33 and ≤73 letters; ≥24 months' follow-up and ≥2 optical coherence tomography (OCT) measurements. OCT scans were analysed using an artificial intelligence algorithm that quantified the volumes of intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachments (PED) and central subfield thickness (CSFT). IRF, SRF and PED were summed to obtain total fluid (TF). The standard deviation (SD) of IRF, SRF, PED, CSFT and TF was computed and categorised into quartiles (SD-Q). Relationships between SD-Qs for each OCT feature and VA change was tested using generalised estimating equations and linear regression. RESULTS: By Month 24, compared to SD-Q1, eyes in SD-Q2, SD-Q3, and SD-Q4 for IRF, SRF, PED, CSFT and TF showed greater VA losses. Eyes in SD-Q4 of TF were 9.4 letters worse compared to eyes in Q1 (95% Confidence Interval: -12.9 to -6.0). The frequency of clinic visits with IRF and SRF present on OCT scans by quartiles of CSFT was lower in eyes with least fluctuation (Q1) compared to eyes with the most fluid fluctuation (Q4) (median [IQR] IRF: 0.3 [0.0-0.7] versus 0.8 [0.5-1.0]; SRF: 0.0 [0.0-0.5] versus 0.6 [0.3-1.0]). CONCLUSIONS: Greater fluctuations in retinal fluid volumes during the maintenance phase of anti-VEGF treatment in nAMD is associated with worse VA by 2 years.

Toward earlier detection of choroidal neovascularization secondary to age-related macular degeneration: multicenter evaluation of a preferential hyperacuity perimeter designed as a home device.

PURPOSE: The primary purpose of this study was to evaluate the ability of a home device preferential hyperacuity perimeter to discriminate between patients with choroidal neovascularization (CNV) and intermediate age-related macular degeneration (AMD), and the secondary purpose was to investigate the dependence of sensitivity on lesion characteristics. METHODS: All participants were tested with the home device in an unsupervised mode. The first part of this work was retrospective using tests performed by patients with intermediate AMD and newly diagnosed CNV. In the second part, the classifier was prospectively challenged with tests performed by patients with intermediate AMD and newly diagnosed CNV. The dependence of sensitivity on lesion characteristics was estimated with tests performed by patients with CNV of both parts. RESULTS: In 66 eyes with CNV and 65 eyes with intermediate AMD, both sensitivity and specificity were 0.85. In the retrospective part (34 CNV and 43 intermediate AMD), sensitivity and specificity were 0.85 +/- 0.12 (95% confidence interval) and 0.84 +/- 0.11 (95% confidence interval), respectively. In the prospective part (32 CNV and 22 intermediate AMD), sensitivity and specificity were 0.84 +/- 0.13 (95% confidence interval) and 0.86 +/- 0.14 (95% confidence interval), respectively. Chi-square analysis showed no dependence of sensitivity on type (P = 0.44), location (P = 0.243), or size (P = 0.73) of the CNV lesions. CONCLUSION: A home device preferential hyperacuity perimeter has good sensitivity and specificity in discriminating between patients with newly diagnosed CNV and intermediate AMD. Sensitivity is not dependent on lesion characteristics.

Signal deconvolution based expression-detection and background adjustment for microarray data.

Background adjustment is an essential stage in analyzing DNA microarrays. Discriminating expressed genes from unexpressed ones (expression detection), and estimating the expression levels of weakly expressed genes, critically depend on accurate treatment of the background intensity. Current methods for background adjustment either do not deal with nonspecific hybridization or strongly depend on the reliability of control probes. Existing model-based methods have limited accuracy. A new platform-independent background adjustment algorithm is presented. The algorithm relies on the deconvoluted experimental signal distribution for evaluating the expression probability and adjusting the background of each probe. Considering expression detection, it is shown, for two-channels cDNA arrays and for the Affymetrix GeneChip platform, that the algorithm performs at least as good or better than control-probes-based algorithms. For the Affymetrix GeneChip arrays, it is further shown that the algorithm outperforms the robust multiarray (RMA) expression measure in estimating genomewide expression levels.

Evidence for abundant transcription of non-coding regions in the Saccharomyces cerevisiae genome.

BACKGROUND: Recent studies in a growing number of organisms have yielded accumulating evidence that a significant portion of the non-coding region in the genome is transcribed. We address this issue in the yeast Saccharomyces cerevisiae. RESULTS: Taking into account the absence of a significantly large yeast EST database, we use microarray expression data collected for genomic regions erroneously believed to be coding to study the expression pattern of non-coding regions in the Saccharomyces cerevisiae genome. We find that at least 164 out of 589 (28%) such regions are expressed under specific biological conditions. In particular, looking at the probes that are located opposing other known genes at the same genomic locus, we find that 88 out of 341 (26%) of these genes support antisense transcription. The expression patterns of these antisense genes are positively correlated. We validate these results using RT-PCR on a sample of 6 non-coding transcripts. CONCLUSION: 1. The yeast genome is transcribed on a scale larger than previously assumed. 2. Correlated transcription of antisense genes is abundant in the yeast genome. 3. Antisense genes in yeast are non-coding.

Large-scale unrestricted identification of post-translation modifications using tandem mass spectrometry.

TwinPeaks, a close variant of the SEQUEST protein identification algorithm, is capable of unrestricted, large-scale, identification of post-translation modifications (PTMs). TwinPeaks is applied on a sample of 100441 tandem mass spectra from the HUPO Plasma Proteome Project data set, with full non-redundant human as a reference protein database. With a 3.5% error rate, TwinPeaks identifies a collection of 539 spectra that were not identified by the usual PTM-restricted identification algorithm. At this error rate, TwinPeaks increases the rate of spectra identifications by at least 17.6%, making unrestricted PTM identification an integral part of proteomics.

Intensity-based statistical scorer for tandem mass spectrometry.

We describe a new statistical scorer for tandem mass spectrometry. The scorer is based on the probability that fragments with given chemical properties create measured intensity levels in the experimental spectrum. The scorer's parameters are computed using a fully automated procedure. Benchmarking the new scorer on a large set of experimental spectra, we show that it performs significantly better than the widely used cross-correlation scoring algorithm of Eng et al. (Eng, J. K; McKormack, A. L.; Yates, J. R. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989.).