TORONTO: A trial-oriented multidimensional psychometric testing algorithm.

Bayesian adaptive methods for sensory threshold determination were conceived originally to track a single threshold. When applied to the testing of vision, they do not exploit the spatial patterns that underlie thresholds at different locations in the visual field. Exploiting these patterns has been recognized as key to further improving visual field test efficiency. We present a new approach (TORONTO) that outperforms other existing methods in terms of speed and accuracy. TORONTO generalizes the QUEST/ZEST algorithm to estimate simultaneously multiple thresholds. After each trial, without waiting for a fully determined threshold, the trial-oriented approach updates not only the location currently tested but also all other locations based on patterns in a reference data set. Since the availability of reference data can be limited, techniques are developed to overcome this limitation. TORONTO was evaluated using computer-simulated visual field tests: In the reliable condition (false positive [FP] = false negative [FN] = 3%), the median termination and root mean square error (RMSE) of TORONTO was 153 trials and 2.0 dB, twice as fast with equal accuracy as ZEST. In the FP = FN = 15% condition, TORONTO terminated in 151 trials and was 2.2 times faster than ZEST with better RMSE (2.6 vs. 3.7 dB). In the FP = FN = 30% condition, TORONTO achieved 4.2 dB RMSE in 148 trials, while all other techniques had > 6.5 dB RMSE and terminated much slower. In conclusion, TORONTO is a fast and accurate algorithm for determining multiple thresholds under a wide range of reliability and subject conditions.

Improving the robustness of the Sequentially Optimized Reconstruction Strategy (SORS) for visual field testing.

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24-2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.

Using Fused Data from Perimetry and Optical Coherence Tomography to Improve the Detection of Visual Field Progression in Glaucoma.

Perimetry and optical coherence tomography (OCT) are both used to monitor glaucoma progression. However, combining these modalities can be a challenge due to differences in data types. To overcome this, we have developed an autoencoder data fusion (AEDF) model to learn compact encoding (AE-fused data) from both perimetry and OCT. The AEDF model, optimized specifically for visual field (VF) progression detection, incorporates an encoding loss to ensure the interpretation of the AE-fused data is similar to VF data while capturing key features from OCT measurements. For model training and evaluation, our study included 2504 longitudinal VF and OCT tests from 140 glaucoma patients. VF progression was determined from linear regression slopes of longitudinal mean deviations. Progression detection with AE-fused data was compared to VF-only data (standard clinical method) as well as data from a Bayesian linear regression (BLR) model. In the initial 2-year follow-up period, AE-fused data achieved a detection F1 score of 0.60 (95% CI: 0.57 to 0.62), significantly outperforming (p < 0.001) the clinical method (0.45, 95% CI: 0.43 to 0.47) and the BLR model (0.48, 95% CI: 0.45 to 0.51). The capacity of the AEDF model to generate clinically interpretable fused data that improves VF progression detection makes it a promising data integration tool in glaucoma management.

Investigating the effects of a temperature dependent photodynamic dose: A numerical study.

SIGNIFICANCE: Photodynamic therapy (PDT) and photothermal therapy (PTT) show promise as cancer treatments, but challenges in generating large ablative volumes for deep-seated tumours persist. Using simulations, this study investigates combined PDT and PTT to increase treatment volumes, including the impact of a temperature-dependent PDT dose on the treatment volume radius. APPROACH: A finite-element model, using the open-source SfePy package, was developed to simulate combined interstitial photothermal and photodynamic treatments. Results compared an additive dose model to a temperature-dependent dose model with enhanced PDT dosimetry and examined typical clinical scenarios for possible synergistic effects. RESULTS: Findings revealed that the temperature-dependent dose model could significantly expand the damage radius compared to the additive model, depending on the tissue and drug properties. CONCLUSIONS: Characterizing synergistic effects of PDT and PTT could enhance treatment planning. Future work is ongoing to implement additional variables, such as photosensitizer photobleaching, and spatial and temporally varying oxygenation.

Topical cannabis-based medicines - A novel adjuvant treatment for venous leg ulcers: An open-label trial.

Venous leg ulcers are highly prevalent lower limb integumentary wounds that remain challenging to heal despite the use of evidence-based compression therapies. A multitude of adjuvant treatments has been studied but none have demonstrated enough efficacy to gain adoption into treatment guidelines. Global attention on Cannabis-Based Therapies is increasing and has been driven by quantum scientific advancements in the understanding of the endocannabinoid signalling system. Topical Cannabis-Based Medicines represent a novel treatment paradigm for venous leg ulcers in terms of promoting wound closure. Fourteen complex patients with sixteen recalcitrant leg ulcers were treated with Topical Cannabis-Based Medicines in conjunction with compression bandaging, every second day, to both wound bed and peri-wound tissues. The cohort had a mean age of 75.8 years and was medically complex as reflected by a mean M3 multimorbidity index score of 2.94 and a mean Palliative Performance Scale score of 67.1%. Complete wound closure, defined as being fully epithelialized, was achieved among 11 patients (79%) and 13 wounds (81%) within a median of 34 days. All three remaining patients demonstrated progressive healing trends but were lost to follow-up. The treatments were well tolerated, and no significant adverse reactions were experienced. The rapid wound closure of previously non-healing venous leg ulcers among elderly and highly complex patients suggests that Topical Cannabis-Based Medicines may become effective adjuvants in conjunction with compression therapy. This may also indicate that they may have an even broader role within integumentary and wound management. Therefore, this treatment paradigm warrants being subjected to controlled trials.