Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis—Toward Retinal Metabolic Diagnostics
Abstract
:1. Introduction
2. Materials and Methods
2.1. Clinical Dataset
2.2. Data Acquisition for AI-Based Analysis
2.2.1. Data from Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO)
2.2.2. Data from OCT Angiography (OCT-A)
2.3. Data Analysis Using Different AI Methods
2.3.1. Preparation of FLIO Data
2.3.2. Initial AI Experiments with FLIO Data
2.3.3. Preparation of OCT-A Data and t-Distributed Stochastic Neighbor Embedding
2.3.4. Local Fractal Dimension of OCT-A Data
2.3.5. Support Vector Machine (SVM) for FLIO and OCT-A Data
2.4. Two-Sample T-Test
3. Results
3.1. FLIO
3.1.1. AI-Assessment with CNN and Encoder Networks
3.1.2. AI-Assessment with SVM
3.2. OCT-A
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Unit | Non-Smokers (n = 26) | Smokers (n = 28) | ||||
---|---|---|---|---|---|---|---|
Male | (No. of subjects) | 13 | 15 | ||||
Female | (No. of subjects) | 13 | 15 | ||||
Mean (SD) | Median | IQR | Mean (SD) | Median | IQR | ||
Age | years old | 26.7 (4.1) | 26.5 | 23.0 to 30.3 | 28.5 (4.7) | 28 | 25.0 to 32.0 |
Years smoked | years | 0 | 0 | 0 | 12.0 (4.9) | 10.8 | 9.0 to 15.8 |
Cumulative packs | 0 | 0 | 0 | 2915 (2224) | 2594 | 883 to 4280 |
Feature Set | n | Mean TP | Mean FN | Mean FP | Mean TN | Mean TPR | Mean FPR | Mean Accuracy | |||
---|---|---|---|---|---|---|---|---|---|---|---|
All features | 36 | 32.70 | 23.30 | 21.35 | 30.65 | 58.39% | ±6.44% | 41.06% | ±4.44% | 58.66% | ±4.63% |
FLIO intensity | 18 | 27.30 | 28.70 | 27.90 | 24.10 | 48.75% | ±5.18% | 53.65% | ±6.20% | 47.59% | ±3.30% |
FLIO τm only | 18 | 34.90 | 21.10 | 21.15 | 30.85 | 62.32% | ±4.85% | 40.67% | ±4.48% | 60.88% | ±3.14% |
FLIO τm; SSC | 9 | 33.50 | 22.50 | 33.40 | 18.60 | 59.82% | ±3.50% | 64.23% | ±5.35% | 48.24% | ±3.26% |
FLIO τm; LSC | 9 | 33.25 | 22.75 | 18.90 | 33.10 | 59.38% | ±6.00% | 36.35% | ±4.75% | 61.44% | ±3.91% |
FLIO τm; IR | 8 | 38.80 | 17.20 | 18.05 | 33.95 | 69.29% | ±4.78% | 34.71% | ±5.91% | 67.36% | ±3.04% |
FLIO τm; OR | 8 | 27.25 | 28.75 | 19.05 | 32.95 | 48.66% | ±3.33% | 36.63% | ±4.73% | 55.74% | ±2.68% |
FLIO τm; IR- SSC, OR- LSC | 8 | 35.00 | 21.00 | 17.05 | 34.95 | 62.50% | ±3.66% | 32.79% | ±5.62% | 64.77% | ±3.54% |
FLIO τm; OR- SSC, IR- LSC | 8 | 26.80 | 29.20 | 27.45 | 24.55 | 47.86% | ±5.43% | 52.79% | ±7.36% | 47.55% | ±3.24% |
FLIO τm; T1-SSC, S2-LSC, S1-SSC | 3 | 40.65 | 15.35 | 16.40 | 35.60 | 72.59% | ±3.88% | 31.54% | ±4.49% | 70.60% | ±2.36% |
Feature Set | n | Mean TP | Mean FN | Mean FP | Mean TN | Mean TPR | Mean FPR | Mean Accuracy | |||
---|---|---|---|---|---|---|---|---|---|---|---|
All features | 36 | 11.85 | 16.15 | 9.50 | 42.50 | 42.32% | ±7.34% | 18.27% | ±2.75% | 67.94% | ±3.02% |
FLIO intensity | 18 | 8.15 | 19.85 | 13.45 | 38.55 | 29.11% | ±8.70% | 25.87% | ±3.67% | 58.38% | ±3.97% |
FLIO τm only | 18 | 14.30 | 13.70 | 8.45 | 43.55 | 51.07% | ±3.41% | 16.25% | ±3.25% | 72.31% | ±2.72% |
FLIO τm; SSC | 9 | 6.05 | 21.95 | 14.50 | 37.50 | 21.61% | ±7.78% | 27.88% | ±3.96% | 54.44% | ±3.43% |
FLIO τm; LSC | 9 | 11.75 | 16.25 | 10.15 | 41.85 | 41.96% | ±5.16% | 19.52% | ±4.00% | 67.00% | ±2.60% |
FLIO τm; IR | 8 | 15.30 | 12.70 | 10.70 | 41.30 | 54.64% | ±6.40% | 20.58% | ±2.28% | 70.75% | ±2.72% |
FLIO τm; OR | 8 | 11.95 | 16.05 | 10.95 | 41.05 | 42.68% | ±6.03% | 21.06% | ±5.52% | 66.25% | ±3.49% |
FLIO τm; IR- SSC, OR- LSC | 8 | 18.05 | 9.95 | 6.40 | 45.60 | 64.46% | ±5.23% | 12.31% | ±4.19% | 79.56% | ±3.31% |
FLIO τm; OR- SSC, IR- LSC | 8 | 9.15 | 18.85 | 11.90 | 40.10 | 32.68% | ±5.90% | 22.88% | ±5.47% | 61.56% | ±3.75% |
FLIO τm; T1-SSC, S2-LSC, S1-SSC | 3 | 19.10 | 8.90 | 7.10 | 44.90 | 68.21% | ±6.07% | 13.65% | ±2.91% | 80.00% | ±2.98% |
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Thiemann, N.; Sonntag, S.R.; Kreikenbohm, M.; Böhmerle, G.; Stagge, J.; Grisanti, S.; Martinetz, T.; Miura, Y. Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis—Toward Retinal Metabolic Diagnostics. Diagnostics 2024, 14, 431. https://doi.org/10.3390/diagnostics14040431
Thiemann N, Sonntag SR, Kreikenbohm M, Böhmerle G, Stagge J, Grisanti S, Martinetz T, Miura Y. Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis—Toward Retinal Metabolic Diagnostics. Diagnostics. 2024; 14(4):431. https://doi.org/10.3390/diagnostics14040431
Chicago/Turabian StyleThiemann, Natalie, Svenja Rebecca Sonntag, Marie Kreikenbohm, Giulia Böhmerle, Jessica Stagge, Salvatore Grisanti, Thomas Martinetz, and Yoko Miura. 2024. "Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis—Toward Retinal Metabolic Diagnostics" Diagnostics 14, no. 4: 431. https://doi.org/10.3390/diagnostics14040431