Fundamental accuracy-resolution trade-off for timekeeping devices

From a thermodynamic point of view, all clocks are driven by irreversible processes. Additionally, one can use oscillatory systems to temporally modulate the thermodynamic flux towards equilibrium. Focusing on the most elementary thermalization events, this modulation can be thought of as a temporal probability concentration for these events. There are two fundamental factors limiting the performance of clocks: On the one level, the inevitable drifts of the oscillatory system, which are addressed by finding stable atomic or nuclear transitions that lead to astounding precision of today's clocks. On the other level, there is the intrinsically stochastic nature of the irreversible events upon which the clock's operation is based. This becomes relevant when seeking to maximize a clock's resolution at high accuracy, which is ultimately limited by the number of such stochastic events per reference time unit. We address this essential trade-off between clock accuracy and resolution, proving a universal bound for all clocks whose elementary thermalization events are memoryless.Comment: 5 + 7 pages, 8 figures, published versio

Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affects different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research

A Functional Regression Model of the Retinal Nerve Fiber Layer Thickness in Healthy Subjects

Purpose: A new functional regression model is presented to explain the intersubject variability of the circumpapillary retinal nerve fiber layer (RNFL) thickness in healthy subjects. Methods: To evaluate the functional regression approach we used data from 202 healthy volunteers, divided equally into training samples (TS) and validation samples (VS). Covariates included RNFL, fovea distance, fovea angle, optic disk ratio, orientation and area provided by Fourier-domainoptical coherence tomography, age, and refractive error. Root mean square errors (RMSE) were calculated for each of the 256 sectors and for the 12 clock-hour sectors in the TS and VS and were compared to the RMSE of the previous model and the standard deviation of the raw data. Results: With the functional regression approach, we were able to explain on average 27.4% of the variation in the TS and 25.1% of the variation in the VS. The new model performed better compared to a multivariate linear regression model. It performed best in the superior-temporal and inferior-temporal clock-hour sectors where the percentage of RMSE reduction ranged between 26.3% and 44.1% for the TS and between 20.6% and 35.4% for the VS. Conclusions: The new functional regression approach improves on the multivariate linear regression model and allows an even larger reduction of the amount of intersubject variability, while at the same time using a substantially smaller number of parameters to be estimated. Translational Relevance: The demonstrated reduction of interindividual variation is expected to translate into an improved diagnostic separation between healthy and glaucomatous subjects, but this remains to be demonstrated in further studies.(VLID)467278

Optic nerve head morphology in primary open-angle glaucoma and nonarteritic anterior ischemic optic neuropathy measured with spectral domain optical coherence tomography

Purpose Optic nerve head (ONH) parameters as well as circumpapillary retinal nerve fibre layer (RNFL) thickness values measured with two different spectral domain optical coherence tomography (SDOCT) machines (Spectralis® and Cirrus® OCT) have been compared between two patient groups, primary openangle glaucoma (POAG), nonarteritic anterior ischaemic optic neuropathy (NAION) and healthy controls. A comparison of the performance of the two OCT machines was made. Methods Twenty healthy controls, 20 POAG and 20 NAION patients with comparable visual field defects were included. Comparison between groups was made using anova and post hoc ttests. To evaluate the diagnostic power of OCT to differentiate POAG from NAION, a stepwise linear regression analysis of the rimRNFL correlation with adjusting covariates (optic disc area and age) was performed. Based on the regression formula, the area under the receiver operator characteristic (AUROC) was calculated. Results Both glaucoma and NAION patients showed significantly smaller global RNFL thickness values compared to healthy subjects in ttests (p < 0.001), while only patients with glaucoma showed significantly smaller global ONH parameters for both devices compared to healthy subjects (p < 0.001). Correlation between global ONH parameters was highly statistically significant (r = 0.93), whereas in ttest a statistically significant difference between the two machines was detected (p < 0.001). Area under the receiver operator characteristic revealed a similarly good discrimination between glaucoma and NAION for Spectralis® (0.980) and Cirrus® OCT (0.945). Conclusion NAION patients have similar RNFL thickness values as do glaucomatous eyes, whereas ONH parameters in NAION eyes were similar to those seen in healthy controls. This difference might help discriminating between these two different disease conditions in a chronic disease stadium, and in this regard, none of the two OCT machines performed better.(VLID)339640

Quantitative assessment of depolarization by the retinal pigment epithelium in healthy and glaucoma subjects measured over a large field of view.

We present measurements of depolarization introduced by the retinal pigment epithelium (RPE) over a 45° field of view using polarization sensitive optical coherence tomography. A detailed spatial distribution analysis of depolarization caused by the RPE is presented in a total of 153 subjects including both healthy and diseased eyes. Age and sex related differences in the depolarizing character of the RPE are investigated

Ultrahigh-resolution OCT imaging of the human cornea

We present imaging of corneal pathologies using optical coherence tomography (OCT) with high resolution. To this end, an ultrahigh-resolution spectral domain OCT (UHR-OCT) system based on a broad bandwidth Ti:sapphire laser is employed. With a central wavelength of 800 nm, the imaging device allows to acquire OCT data at the central, paracentral and peripheral cornea as well as the limbal region with 1.2 µm x 20 µm (axial x lateral) resolution at a rate of 140 000 A-scans/s. Structures of the anterior segment of the eye, not accessible with commercial OCT systems, are visualized. These include corneal nerves, limbal palisades of Vogt as well as several corneal pathologies. Cases such as keratoconus and Fuchs’s endothelial dystrophy as well as infectious changes caused by diseases like Acanthamoeba keratitis and scarring after herpetic keratitis are presented. We also demonstrate the applicability of our system to visualize epithelial erosion and intracorneal foreign body after corneal trauma as well as chemical burns. Finally, results after Descemet’s membrane endothelial keratoplasty (DMEK) are imaged. These clinical cases show the potential of UHR-OCT to help in clinical decision-making and follow-up. Our results and experience indicate that UHR-OCT of the cornea is a promising technique for the use in clinical practice, but can also help to gain novel insight in the physiology and pathophysiology of the human cornea.Published versio

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

Background: Optical coherence tomography (OCT) is a retinal imaging system that may improve the diagnosis of multiple sclerosis (MS) persons, but the evidence is currently equivocal. To assess whether compensating the peripapillary retinal nerve fiber layer (pRNFL) thickness for ocular anatomical features as well as the combination with macular layers can improve the capability of OCT in differentiating non-optic neuritis eyes of relapsing-remitting MS patients from healthy controls. Methods: 74 MS participants (n = 129 eyes) and 84 age- and sex-matched healthy controls (n = 149 eyes) were enrolled. Macular ganglion cell complex (mGCC) thickness was extracted and pRNFL measurement was compensated for ocular anatomical factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between groups. Results: Participants with MS showed significantly thinner mGCC, measured and compensated pRNFL (p ≤ 0.026). Compensated pRNFL achieved better performance than measured pRNFL for MS differentiation (AUC, 0.75 vs 0.80; p = 0.020). Combining macular and compensated pRNFL parameters provided the best discrimination of MS (AUC = 0.85 vs 0.75; p < 0.001), translating to an average improvement in sensitivity of 24 percent for differentiation of MS individuals. Conclusion: The capability of OCT in MS differentiation is made more robust by accounting OCT scans for individual anatomical differences and incorporating information from both optic disc and macular regions, representing markers of axonal damage and neuronal injury, respectively.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityNational Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis work was funded by grants from the National Medical Research Council (CG/C010A/2017_SERI; OFIRG/0048/2017; OFLCG/004c/2018; TA/MOH-000249-00/2018 MOH-OFIRG20nov-0014 and NMRC/CG2/004b/2022-SERI), National Research Foundation Singapore (NRF2019-THE002-0006 and NRF-CRP24-2020-0001), A*STAR (A20H4b0141), the Singapore Eye Research Institute & Nanyang Technological University (SERI-NTU Advanced Ocular Engineering (STANCE) Program), the Duke-NUS Medical School (Duke-NUS-KP(Coll)/2018/0009A), and the SERI-Lee Foundation (LF1019-1) Singapore

Assessing the external validity of machine learning-based detection of glaucoma

Abstract Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P < 0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the ML model trained with compensated data (AUC = 0.93 and accuracy = 84%) outperformed the ML model trained with original data (AUC = 0.83 and accuracy = 79%; P < 0.001) and measured data (AUC = 0.82; P < 0.001) for glaucoma detection. The performance with the ML model trained on measured data showed poor reproducibility across different datasets, whereas the performance of the compensated data was maintained. Care must be taken when ML models are applied to patient cohorts of different ethnicities

A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer's disease

10.1186/s13195-022-00982-0ALZHEIMERS RESEARCH & THERAPY14