Pairs Of Scans Research Articles

Accuracy of different maxillomandibular relationship recording techniques in the edentulous maxillary arch.

This study investigated the effect of three digital bite registration techniques on the accuracy of intraoral scanning in maxillary edentulous and mandibular dentate arches. Maxillary edentulous and mandible fully dentate models were created. Four dental implants were placed in the maxilla, fitted with scan bodies, and reference scans were obtained using a Nikon Altera 10.7.6 scanner. Digital impressions were acquired 10 times for each model using a Trios 4 intraoral scanner. Three different digital bite records were collected for each pair of mandible and maxilla scans. The first one was a bite record without additional material (WSB), the second utilized a silicone index between the models (WSB silicone), and the third followed a pre-preparation scanning protocol (Pre-PREP). The data was exported in standard tessellation language (STL) format, which was assessed for trueness and precision using statistical analysis. According to mean trueness values, WSB exhibited the lowest value, 173 µm, followed by WSB silicone 242 µm and Pre-PREP 620 µm. The differences were significant only when comparing Pre-PREP to WSB and Pre-PREP to WSB silicone (p <0.05). Regarding precision, WSB demonstrated a mean value of 171.5 µm, followed by WSB silicone with 213.8 µm, and Pre-PREP with 222.2 µm with no significant difference between the groups. The presence of interarch bite registration material adversely affected interocclusal record accuracy, while WSB silicone presented a suitable substitute for WSB.

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions

Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a retrieval system and encoders that produce comprehensive OCT and fundus image representations that generalize well for various downstream tasks on three independent external datasets, explicitly focusing on clinically pertinent prediction tasks. In addition, we show that interchanging OCT with lower-cost fundus imaging can preserve the predictive power of the trained models.

Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses.

Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.

Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset

BackgroundProximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam—Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations.MethodsThe ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans.ResultsMicro-CT support improved interobserver agreement (Cohen’s Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59–0.68) versus 0.46 (95% CI: 0.44–0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34–51%) to 63% (95% CI: 54–71%) and from 92% (95% CI: 88–95%) to 95% (95% CI: 92–97%), respectively.ConclusionThe ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability.Graphical

Scan-rescan variability of AI-enabled cardiac functional assessment from cine CMR

Abstract Background/Introduction Cine cardiovascular magnetic resonance (CMR) is the gold-standard technique for the assessment of cardiac function and structure. Manual analysis of cine CMR to estimate structural and functional biomarkers is costly and time-consuming, motivating the use of artificial intelligence (AI) for automation. However, the scan-rescan repeatability of AI derived biomarkers remains unknown. Purpose Measure the repeatability of AI-based cine CMR biomarkers in scan-rescan data. Methods 92 scan-rescan short-axis cine CMR were acquired from volunteers on the same day (i.e. 184 in total) and annotated with ground truth (GT) manual segmentations of the left and right ventricle (LV, RV) blood pools, and LV myocardium at end-diastole (ED) and end-systole (ES). AI segmentations and subsequent biomarker estimates were produced using the AI-CMR-QC tool described in [1]. Segmentation performance was assessed with Dice score, pixelwise sensitivity and specificity, and Hausdorff distance. Scan-rescan differences in cardiac biomarkers for each subject were used to assess repeatability. Repeatability of LV ejection fraction (EF) based classification (LVEF&amp;lt;40 = abnormal, 40&amp;gt;LVEF&amp;gt;50 = mildly reduced, LVEF&amp;gt;50 = normal) [2] was also estimated from scan-rescan data. Results The segmentation model obtained high Dice scores (&amp;gt; 88% for LV and RV blood pools, &amp;gt;83% for LV myocardium). The summary of scan-rescan biomarker differences in Fig. 1(a) highlights that biomarker repeatability is generally better in GT segmentations, with lower differences between the two scans. While performance is satisfactory, a more in-depth analysis reveals that in some pairs of scans there are inconsistencies in the segmentations, leading to fluctuations in the calculation of blood pool volumes, which affects the repeatability of the computed biomarkers, as seen in Fig. 2(b). For instance, significant changes in LVEF between pairs of scans can arise from inconsistencies in the segmentation of the LV in basal slices, as shown in Fig. 2(a). This has consequences in the classification of subjects based on LVEF, as shown in Fig. 1(b). Only two subjects belonged to the mildly reduced LVEF class, however, when comparing predicted scan and rescan LVEF, both sensitivity and specificity decrease, suggesting inconsistency in the biomarker estimation. Conclusion(s) While we have demonstrated successful AI cardiac segmentation and biomarker estimation based on conventional performance metrics, additional refinement is necessary to enhance reliability and consistency. Further work is needed to obtain a better delineation of the valve plane, which would help addressing the inconsistencies at the LV basal slice. An automated segmentation and analysis pipeline for cine CMR is vital to support overworked clinicians, however, demonstrably repeatable performance, particularly when looking at therapy response, will have a significant impact on patient care and clinical outcomes.Repeatability of GT and AI biomarkersRepeatability of GT and AI segmentations

T2 mapping as a marker of active myocardial injury in Anderson-Fabry disease patients

Abstract Background In Anderson-Fabry Disease (AFD) with cardiac involvement, left ventricular hypertrophy (LVH) is often associated with myocardial scarring by cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging. Myocardial inflammation, detected as increased T2 weighted/T2 mapping (T2m) signal, has also been reported, although its role in disease progression is still unclear. Purpose To investigate the relationship between T2m and LGE/LVH progression in AFD patients. Methods 67 paired CMR scans (1.5T) from 30 AFD patients were included. Three groups were defined according to LGE: no LGE at either scan (LGE-/-); LGE at baseline, unchanged at follow-up (LGE+/-); LGE at follow-up, appeared or increased compared to baseline (LGE+/+). T2m was measured in the infero-lateral basal segment in all scans; in LGE+ scans only, LGE was also manually contoured (ROI_LGE), and the ROI_LGE copied and pasted on the matching T2m image (T2m_ROI_LGE). See Figure 1. Troponin I (TnI) was also dosed at each scan. A univariate repeated measures analysis of variance was used to compare the groups and Tukey-Kramer correction was applied. Results As expected, the presence of LGE was strongly associated with older age, higher global and regional T2m, left ventricular (LV) mass and TnI values. As a novelty, in the LGE+/+ scans (scar appearance/progression) T2m in the ROI_LGE was higher than in the LGE+/- scans (scar unchanged) (p value 0.02). None of the other parameters, such as LV mass, TnI and global T2m values, differ between the the LGE+/+ compared to the LGE +/- group. See Table 1. Conclusions In our AFD patients’ cohort, scar progression was associated with focal increase in T2m values, i.e. T2m may identify patients with active myocardial injury. The potential implication on management/disease progression monitoring needs to be tested in future research, with longer follow-up. The role of sex, with females known to have higher average T2m values, will also need to be explored in a wider patient’s cohort.

AI-enabled automated cardiac chambers volumetry in non-contrast ECG-gated cardiac scans vs. non-contrast non-gated lung scans

Abstract Introduction We have developed an AI-enabled automated volume measurement of cardiac chambers (AutoChamber) that works on ECG-gated coronary artery calcium scans and correlates well with contrast enhanced coronary CT angiography scans. We have recently reported that increased left atrial volume measured by AutoChamber is a strong predictor of new onset atrial fibrillation (AF) in asymptomatic individuals of Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we compare the volumetry results in ECG- gated CAC scans with those of non-gated full-chest lung cancer screening scans in the same individuals. Methods We have studied 169 cases of paired ECG-gated cardiac CT scans and non-gated lung scans obtained from Harbor UCLA. Mean±SD for age was 62.1±10.0 years with 52% female participants. All cases were asymptomatic and were scanned for preventive health assessment. AutoChamber was run on all cases by an independent operator who was not involved in data analysis. P value was calculated using a two-tailed test of significance with α=0.05. Results AutoChamber in cardiac scans vs lung scans reported volume (Mean±SD) for left atrium (LA) was 63.8 ± 18.5 vs. 65.5 ± 19.4, left ventricle (LV) 102.8 ± 25.3 vs. 105.5 ± 25.2, right atrium (RA) 81.9 ± 20.5 vs. 85.7 ± 21.9 right ventricle (RV), 140.7 ± 34.2 vs. 134.2 ± 33.7, left ventricle wall (LVW) 113.0 ± 26.6 vs. 109.7 ± 27.0 respectively (P &amp;lt; 0.0001). Strong correlation was shown between cardiac and lung scans for each cardiac chamber: LA (R = 0.92), LV (R = 0.93), RA (R = 0.91), RV (R = 0.92), and left ventricular wall (LVW) R = 0.95. Conclusions AutoChamber volumetry results are similar in ECG-gated cardiac scans vs non-gated full-chest lung scans. This AI-enabled automated tool is promising as it can provide added value to patients undergoing coronary calcium and lung cancer screening scans flagging enlarged cardiac chambers at risk of AF and heart failure.

Pediatric contrast-enhanced chest CT on a photon-counting detector CT: radiation dose and image quality compared to energy-integrated detector CT.

Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. Age (14.2years vs 13.8years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7mGy (IQR 1.1-2.4mGy) vs 3.8mGy (IQR 2.0-4.7mGy) (P< 0.01), and size-specific dose estimate, 2.6mGy (IQR 1.8-3.1mGy) vs 5.0mGy (IQR 3.3-6.2mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

G.O.O.S.E.: Introducing a novel five‐stage structured framework for the early phases of teaching and learning ultrasound

AbstractThe sonography profession faces a critical global workforce shortage, particularly pronounced in Australia, due to insufficient clinical training opportunities and an ageing workforce. The financial burden and increased scan times associated with training exacerbate this issue. Innovative approaches, such as intensive ultrasound courses and pair scanning protocols, have shown promise in addressing these challenges. The GOOSE framework (Get the window, Optimise the view, Optimise the image, Select the image, Explain the findings) offers a structured, step‐by‐step approach to practical sonography training. This framework deconstructs complex ultrasound skills into manageable tasks, optimises cognitive load management, and provides explicit guidance for novice learners. The GOOSE framework is designed to enhance the efficiency of teaching and learning by simplifying complex skills into clear, actionable steps, supported by checklists that guide both instructors and students through the training process. The GOOSE framework offers a systematisation of knowledge transfer that, combined with deliberate practice and structured feedback, ensures learners progressively build their skills with focused, goal‐directed activities. While this model is yet to be validated, initial implementations have shown promising results in improving trainee performance, reducing supervision workload, and standardising instructional methods. This article outlines the principles of the GOOSE framework, its development, and its application in a specialised training facility, proposing it as a standardised model to enhance practical ultrasound education and improve teaching and learning efficiency.

PET mapping of receptor occupancy using joint direct parametric reconstruction.

In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and postdrug injection dynamic PET scans. The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

Evaluation of ComBat harmonization for reducing across-tracer differences in regional amyloid PET analyses.

Introduction Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. Conclusion ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.

Evaluation of magnetic resonance thermometry performance during MR-guided hyperthermia treatment of soft-tissue sarcomas in the lower extremities and pelvis

Introduction This study evaluated the performance of magnetic resonance thermometry (MRT) during deep-regional hyperthermia (HT) in pelvic and lower-extremity soft-tissue sarcomas. Materials and methods 17 pelvic (45 treatments) and 16 lower-extremity (42 treatments) patients underwent standard regional HT and chemotherapy. Pairs of double-echo gradient-echo scans were acquired during the MR protocol 1.4 s apart. For each pair, precision was quantified using phase data from both echoes (‘dual-echo’) or only one (‘single-echo’) in- or excluding body fat pixels in the field drift correction region of interest. The precision of each method was compared to that of the MRT approach using a built-in clinical software tool (SigmaVision). Accuracy was assessed in three lower-extremity patients (six treatments) using interstitial temperature probes. The Jaccard coefficient quantified pretreatment motion; receiver operating characteristic analysis assessed its predictability for acceptable precision (<1 °C) during HT. Results Compared to the built-in dual-echo approach, single-echo thermometry improved the mean temporal precision from 1.32 ± 0.40 °C to 1.07 ± 0.34 °C (pelvis) and from 0.99 ± 0.28 °C to 0.76 ± 0.23 °C (lower extremities). With body fat-based field drift correction, single-echo mean accuracy improved from 1.4 °C to 1.0 °C. Pretreatment bulk motion provided excellent precision prediction with an area under the curve of 0.80–0.86 (pelvis) and 0.81–0.83 (lower extremities), compared to gastrointestinal air motion (0.52–0.58). Conclusion Single-echo MRT exhibited better precision than dual-echo MRT. Body fat-based field-drift correction significantly improved MRT accuracy. Pretreatment bulk motion showed improved prediction of acceptable MRT temporal precision over gastrointestinal air motion.

Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer’s disease progression from longitudinal MRI

Abstract Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer’s disease (AD). Estimating brain progression patterns can be applied to understanding the therapeutic effects of amyloid-clearing drugs in research and detecting the earliest sign of accelerated atrophy in clinical settings. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative interscan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring and progression understanding in preclinical AD.

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

ObjectivesTrain an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess...

Unfolding the mysterious roles of GeTe/SnTe compositing within CuInTe2 thermoelectric alloy: Short-range and local chemical orders

The optimization of thermoelectric (TE) properties in TE materials requires controlling intrinsic factors such as order/disorder in crystal and chemical structures. Although heterogeneous interfaces play a key role in regulating TE properties, the corresponding atomic disorder-order has been ignored in composited systems. We demonstrate examples of GeTe/SnTe compositing for tuning CuInTe2 TE properties. Evidences of increased short-range order and nanoclusters with local chemical order are presented via synchrotron X-ray pair distribution function and scanning transmission electron microscopy. Specifically, GeTe/SnTe compositing within CuInTe2 enhances short-range order. Nanodomains of CuInTe2(GeTe)x are accompanied by a significant atomic-intensity fluctuation that intensifies the scattering of phonons. In contrast to CuInTe2(GeTe)x, nanoclusters with local chemical order are observed in CuInTe2(SnTe)y, accompanied by a weak atomic-intensity fluctuation, which provides superior carrier transport channels and power factor for CuInTe2(SnTe)0.01. Ultimately, a maximum thermoelectric figure of merit zT of ∼1.0 is reported for CuInTe2(SnTe)0.01, which is 54 % greater than that of pristine CuInTe2, and higher than the 0.86 for CuInTe2(GeTe)0.01. This work demonstrates new disorder-order insights that reveal the mysterious roles of compositing for tuning TE transport.

Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes.

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

Automated monitoring of tooth wear progression using AI on intraoral scans

ObjectivesThis study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. MethodsEight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. ResultsThe automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. ConclusionsThe proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. Clinical significanceGeneral practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Measuring Geographic Atrophy Area Using Column-Based Machine Learning Software on Spectral-Domain Optical Coherence Tomography versus Fundus Auto Fluorescence.

The purpose of this study was to compare geographic atrophy (GA) area semi-automatic measurement using fundus autofluorescence (FAF) versus optical coherence tomography (OCT) annotation with the cRORA (complete retinal pigment epithelium and outer retinal atrophy) criteria. GA findings on FAF and OCT were semi-automatically annotated at a single time point in 36 pairs of FAF and OCT scans obtained from 36 eyes in 24 patients with dry age-related macular degeneration (AMD). The GA area, focality, perimeter, circularity, minimum and maximum Feret diameter, and minimum distance from the center were compared between FAF and OCT annotations. The total GA area measured on OCT was 4.74 ± 3.80 mm2. In contrast, the total GA measured on FAF was 13.47 ± 8.64 mm2 (p < 0.0001), with a mean difference of 8.72 ± 6.35 mm2. Multivariate regression analysis revealed a significant correlation between the difference in area between OCT and FAF and the total baseline lesion perimeter and maximal lesion diameter measured on OCT (adjusted r2: 0.52; p < 0.0001) and the total baseline lesion area measured on FAF (adjusted r2: 0.83; p < 0.0001). We report that the GA area measured on FAF differs significantly from the GA area measured on OCT. Further research is warranted in order to determine the clinical relevance of these findings.

Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.

To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning. We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship between macular thinning and paracentral VF loss in glaucoma. The average mean absolute error and R2 for 68 10-2 VF test points were 3.30 ± 0.52dB and 0.70 ± 0.11, respectively. The accuracy was lower in the inferior temporal region. The model placed greater emphasis on 24-2 VF points near the central fixation point when predicting the 10-2 VFs. The inclusion of nine VF test features improved the mean absolute error and R2 up to 0.17 ± 0.06dB and 0.01 ± 0.01, respectively. Age was the most important 24-2 VF test parameter for 10-2 VF prediction. The predicted 10-2 VFs achieved an improved structure-function relationship between macular thinning and paracentral VF loss, with the R2 at the central 4, 12, and 16 locations of 24-2 VFs increased by 0.04, 0.05 and 0.05, respectively (P < 0.001). The 10-2 VFs may be predicted from 24-2 data. The predicted 10-2 VF has the potential to improve glaucoma diagnosis.

Comparison between Spectral-Domain and Swept-Source OCT Angiography for the Measurement of Persistent Hypertransmission Defects in Age-Related Macular Degeneration

Spectral-domain OCT angiography (SD-OCTA) scans were tested in an algorithm developed for use with swept-source OCT angiography (SS-OCTA) scans to determine if SD-OCTA scans yielded similar results for the detection and measurement of persistent choroidal hypertransmission defects (hyperTDs). Retrospective study. Forty pairs of scans from 32 patients with late-stage nonexudative age-related macular degeneration (AMD). Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6× 6 mm OCTA scan patterns. Using a semiautomatic algorithm that helped with outlining the hyperTDs, 2 graders independently validated persistent hyperTDs, which are defined as having a greatest linear dimension ≥250 μm on the en face images generated using a slab extending from 64 to 400 μm beneath Bruch's membrane. The number of lesions and square root (sqrt) total area of the hyperTDs were obtained from the algorithm using each imaging method. The mean sqrt area measurements and the number of hyperTDs were compared. The number of lesions and sqrt total area of the hyperTDs were highly concordant between the 2 instruments (rc= 0.969 and rc= 0.999, respectively). The mean number of hyperTDs was 4.3 ± 3.1 for SD-OCTA scans and 4.5 ± 3.3 for SS-OCTA scans (P= 0.06). The mean sqrt total area measurements were 1.16 ± 0.64 mm for the SD-OCTA scans and 1.17 ± 0.65 mm for the SS-OCTA scans (P < 0.001). Because of the small standard error of the differences, the mean difference between the scans was statistically significant but not clinically significant. Spectral-domain OCTA scans provide similar results to SS-OCTA scans when used to obtain the number and area measurements of persistent hyperTDs through a semiautomated algorithm previously developed for SS-OCTA. This facilitates the detection of atrophy with a more widely available scan pattern and the longitudinal study of early to late-stage AMD. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.