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Continuous 24-Hour Intraocular Pressure Monitoring with a Smart Contact Lens Predicts Visual Field Progression in Normal-Tension Glaucoma Taryudi Suharyana; Agnes Mariska
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Opthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.135

Introduction: Normal-tension glaucoma (NTG) may progress despite normal office intraocular pressure (IOP), suggesting that 24-hour IOP dynamics captured by a smart contact lens (SCL) sensor may improve risk stratification. This study evaluated whether SCL-derived nocturnal IOP parameters independently predict Humphrey 24-2 visual field (VF) progression in NTG. Methods: This prospective observational cohort enrolled 62 NTG eyes (62 patients; one eye per patient) who underwent 24-hour SCL monitoring and serial Humphrey 24-2 SITA-Standard VF testing over 24 months at a private hospital in Palembang, Indonesia. The primary outcome was VF mean deviation (MD) slope (dB/year). Multivariable linear regression and receiver operating characteristic (ROC) analysis were performed. Results: Eyes with a nocturnal IOP acrophase (n = 36) had a significantly faster MD decline (−1.24 ± 0.31 dB/year) than eyes without acrophase (n = 26; −0.42 ± 0.19 dB/year; mean difference −0.82 dB/year, 95% CI −0.97 to −0.67, p < 0.001; Cohen d = 3.20). Independent predictors of MD slope were nocturnal acrophase (β = −0.71, p < 0.001), number of long peaks (β = −0.18, p < 0.001), and baseline RNFL thickness (β = +0.034, p = 0.002); adjusted R² = 0.64. Nocturnal amplitude yielded an AUC of 0.83 (95% CI 0.74–0.91). Conclusion: SCL-derived nocturnal IOP parameters independently predict VF progression in NTG. Integration of 24-hour SCL monitoring may enhance risk stratification beyond office IOP measurement.

Diffusion Tensor Imaging and Resting-State Functional MRI Reveal Coupled Microstructural and Default-Mode Network Alterations in Mild Cognitive Impairment: A Diagnostic Accuracy Study Taryudi Suharyana; Akmal Hasan; Jason Willmare
Sriwijaya Journal of Radiology and Imaging Research Vol. 3 No. 2 (2025): Sriwijaya Journal of Radiology and Imaging Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjrir.v3i2.285

Introduction: Early-stage neurodegeneration — clinically expressed as mild cognitive impairment (MCI) — lacks accessible imaging biomarkers that link microstructural white-matter injury to functional network disruption. We evaluated whether integrating diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) at 3.0-Tesla improves detection of MCI and substantiates a structural-to-functional disconnection mechanism. Methods: In this STARD-2015-compliant, prospective cross-sectional diagnostic accuracy study at a tertiary hospital in Palembang, Indonesia, 85 participants (45 MCI, 40 age-, sex- and education-matched controls) underwent single-scanner 3.0-T MRI. DTI metrics (fractional anisotropy [FA], mean diffusivity [MD]) were derived by tract-based spatial statistics and posterior-cingulate-seeded Default Mode Network (DMN) connectivity by CONN. Consensus clinical diagnosis (Petersen criteria) was the blinded reference standard. Diagnostic accuracy used ROC/DeLong AUC, Wilson 95% confidence intervals (CIs), Cohen κ, and multivariable logistic regression. Results: Posterior-cingulum FA was lower (0.399 ± 0.058 vs 0.489 ± 0.052, p<0.001) and MD higher in MCI; PCC–mPFC connectivity was reduced (0.392 ± 0.124 vs 0.586 ± 0.136, p<0.001). FA discriminated MCI with AUC 0.903 (95% CI 0.831–0.974; sensitivity 82.2%, specificity 90.0%); a combined FA+FC model reached AUC 0.901 with sensitivity 95.6% and NPV 93.8% but did not exceed FA alone (DeLong p=0.90). FA and connectivity were strongly correlated (r=0.79, 95% CI 0.69–0.86, p<0.001). Inter-reader agreement was substantial (κ=0.74 and 0.67). Conclusion: Multimodal 3.0-T DTI and rs-fMRI provides an accurate, radiation-free signature of early-stage neurodegeneration; the coupling between cingulum microstructure and DMN connectivity is consistent with structural disconnection being associated with functional decoupling and offers a deployable tool for tertiary referral centres.

Continuous 24-Hour Intraocular Pressure Monitoring with a Smart Contact Lens Predicts Visual Field Progression in Normal-Tension Glaucoma Taryudi Suharyana; Agnes Mariska
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Ophthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.135

Introduction: Normal-tension glaucoma (NTG) may progress despite normal office intraocular pressure (IOP), suggesting that 24-hour IOP dynamics captured by a smart contact lens (SCL) sensor may improve risk stratification. This study evaluated whether SCL-derived nocturnal IOP parameters independently predict Humphrey 24-2 visual field (VF) progression in NTG. Methods: This prospective observational cohort enrolled 62 NTG eyes (62 patients; one eye per patient) who underwent 24-hour SCL monitoring and serial Humphrey 24-2 SITA-Standard VF testing over 24 months at a private hospital in Palembang, Indonesia. The primary outcome was VF mean deviation (MD) slope (dB/year). Multivariable linear regression and receiver operating characteristic (ROC) analysis were performed. Results: Eyes with a nocturnal IOP acrophase (n = 36) had a significantly faster MD decline (−1.24 ± 0.31 dB/year) than eyes without acrophase (n = 26; −0.42 ± 0.19 dB/year; mean difference −0.82 dB/year, 95% CI −0.97 to −0.67, p < 0.001; Cohen d = 3.20). Independent predictors of MD slope were nocturnal acrophase (β = −0.71, p < 0.001), number of long peaks (β = −0.18, p < 0.001), and baseline RNFL thickness (β = +0.034, p = 0.002); adjusted R² = 0.64. Nocturnal amplitude yielded an AUC of 0.83 (95% CI 0.74–0.91). Conclusion: SCL-derived nocturnal IOP parameters independently predict VF progression in NTG. Integration of 24-hour SCL monitoring may enhance risk stratification beyond office IOP measurement.

Neurocomputational Decoding of Feigned Psychosis: Frontoparietal Cognitive Control Activation and Support Vector Machine Classification for Objective Forensic Symptom Validity Assessment in Indonesia Taufiq Indera Jayadi; Taryudi Suharyana; Vita Amanda; Brenda Jaleel
Sriwijaya Journal of Forensic and Medicolegal Vol. 2 No. 2 (2024): Sriwijaya Journal of Forensic and Medicolegal
Publisher : Phlox Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjfm.v2i2.360

Background. Detecting feigned psychiatric symptoms is a high-stakes problem in Indonesian forensic evaluation (visum et repertum psychiatricum). Current assessment relies on clinical interview and psychometric symptom validity tests, which can be manipulated. Objective. To examine whether functional magnetic resonance imaging (fMRI) combined with supervised machine learning can separate feigned from genuine psychosis in an Indonesian forensic cohort. Methods. In this multi-center case-control study, 90 right-handed men formed three groups of 30: malingered psychosis (MAL), genuine psychosis (PAT) and healthy controls (HC). Participants completed clinical and symptom validity assessment and a 3 T block-design symptom endorsement task. Activation (MAL > PAT) was modeled in SPM12. Support vector machine (SVM) and random forest classifiers using regional activation and connectivity features were evaluated with stratified 10-fold cross-validation. Results. MAL > PAT showed greater activation in the dorsal anterior cingulate cortex (Z = 5.31), bilateral dorsolateral and ventrolateral prefrontal cortex, anterior insula and inferior parietal lobule (family-wise error cluster-corrected p < 0.05). A radial basis function SVM on combined features classified MAL versus PAT with accuracy 83.3% (50/60), sensitivity 80.0%, specificity 86.7% and area under the curve (AUC) 0.88; a random forest reached 81.7% (49/60). Estimates come from one cross-validation without independent validation, and MAL was defined by psychometric tests. Conclusion. Frontoparietal and cingulo-insular activation patterns are candidate markers that separated feigned from genuine psychosis in this sample and are consistent with greater cognitive-control demand during feigned endorsement. They may become a useful adjunct to conventional evaluation only after independent validation.

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