Manual Tracing Research Articles

Development of a semi-automatic segmentation technique based on mean magnetic resonance imaging intensity thresholding for volumetric quantification of plexiform neurofibromas

Rationale and objectivesPlexiform neurofibromas (PNs) are peripheral nerve tumors that occur in 25–50 % of patients with neurofibromatosis type 1. PNs may have complex, diffused, and irregular shapes. The objective of this work was to develop a volumetric quantification method for PNs as clinical assessment is currently based on unidimensional measurement. Materials and methodsA semi-automatic segmentation technique based on mean magnetic resonance imaging (MRI) intensity thresholding (SSTMean) was developed and compared to a similar and previously published technique based on minimum image intensity thresholding (SSTMini). The performance (volume and computation time) of the two techniques was compared to manual tracings of 15 tumors of different locations, shapes, and sizes. Performance was also assessed using different MRI sequences. Reproducibility was assessed by inter-observer analysis. ResultsWhen compared to manual tracing, quantification performed with SSTMean was not significantly different (mean difference: 1.2 %), while volumes computed by SSTMini were significantly different (p < .0001, mean difference: 13.4 %). Volumes quantified by SSTMean were also significantly different than the ones assessed by SSTMini (p < .0001). Using SSTMean, volumes quantified with short TI inversion recovery, T1-, and T2-weighted imaging were not significantly different. Computation times used by SSTMean and SSTMini were significantly lower than for manual segmentation (p < .0001). The highest difference measured by two users was 8 cm3. ConclusionOur method showed accuracy compared to a current gold standard (manual tracing) and reproducibility between users. The refined segmentation threshold and the possibility to define multiple regions-of-interest to initiate segmentation may have contributed to its performance. The versatility and speed of our method may prove useful to better monitor volumetric changes in lesions of patients enrolled in clinical trials to assessing response to therapy.

Use of a novel magnetically actuated compression system to study the temporal dynamics of axial and lateral strain in human osteochondral plugs

The high water content of articular cartilage allows this biphasic tissue to withstand large compressive loads through fluid pressurization. The system presented here, termed the “MagnaSquish”, provides new capabilities for quantifying the effect of rehydration on cartilage behavior during cyclic loading. An imbalanced rate of fluid exudation during load and fluid re-entry during recovery can lead to the accumulation of strain during successive loading cycles - a phenomenon known as ratcheting. Typical experimental systems for cartilage biomechanics use continuous contact between the platen and sample, which may affect tissue rehydration by compressing the top layer of cartilage and slowing fluid re-entry. To address this limitation, we developed a magnetically actuated device that provides full lift-off of the platen in between loading cycles. We investigated strain accumulation in cadaveric human osteochondral plugs during 750 loading cycles, with two dimensional profiles of the cartilage captured at 30 frames per second throughout loading and 10 min of additional free swelling recovery. Axial and lateral strain measurements were extracted from the tissue profiles using a UNet-based deep learning algorithm to circumvent manual tracing. We observed increased axial strain accumulation with shorter inter-cycle recovery, with static loading serving as the extreme case of zero recovery. The loading waveform during the 750 cycles dictated the pace of the recovery during the extended free swelling period, as shorter inter-cycle recovery led to more persistent axial strain accumulation for up to five minutes. This work showcases the importance of fluid re-entry in resisting strain accumulation during cyclical compression.

Comparison of automated and manual quantification methods for neuromelanin-sensitive MRI in Parkinson's disease.

Neuromelanin-sensitive magnetic resonance imaging quantitative analysis methods have provided promising biomarkers that can noninvasively quantify degeneration of the substantia nigra in patients with Parkinson's disease. However, there is a need to systematically evaluate the performance of manual and automated quantification approaches. We evaluate whether spatial, signal-intensity, or subject specific abnormality measures using either atlas based or manually traced identification of the substantia nigra better differentiate patients with Parkinson's disease from healthy controls using logistic regression models and receiver operating characteristics. Inference was performed using bootstrap analyses to calculate 95% confidence interval bounds. Pairwise comparisons were performed by generating 10,000 permutations, refitting the models, and calculating a paired difference between metrics. Thirty-one patients with Parkinson's disease and 22 healthy controls were included in the analyses. Signal intensity measures significantly outperformed spatial and subject specific abnormality measures, with the top performers exhibiting excellent ability to differentiate patients with Parkinson's disease and healthy controls (balanced accuracy = 0.89; area under the curve = 0.81; sensitivity =0.86; and specificity = 0.83). Atlas identified substantia nigra metrics performed significantly better than manual tracing metrics. These results provide clear support for the use of automated signal intensity metrics and additional recommendations. Future work is necessary to evaluate whether the same metrics can best differentiate atypical parkinsonism, perform similarly in de novo and mid-stage cohorts, and serve as longitudinal monitoring biomarkers.

Cerebral microbleeds in Down syndrome: association with demographic, genetic, fluid and imaging biomarkers of Alzheimer’s disease

AbstractBackgroundWith the development of amyloid‐modifying therapeutic agents that can lead to cerebral microhaemorrhages, it is critical to meticulously characterise the natural history of cerebral microbleeds (MBs) in populations genetically predisposed to develop Alzheimer’s disease (AD), such as Down syndrome (DS). We aimed to investigate MB prevalence in a large cohort of adults with DS and define associations with demographic, genetic, fluid and imaging biomarkers of AD, and cognitive performance.MethodsCross‐sectional design. Adults with DS and euploid controls from the Down Alzheimer Barcelona Neuroimaging Initiative underwent a 3T‐MRI protocol, including susceptibility‐weighted imaging (SWI) acquisition. Manual tracing of MBs was performed by two independent raters using ITK‐SNAP. Participants were classified according to MB status (MB‐/MB+) and severity (0, 1, 2+ MBs). Non‐parametric statistical tests were used to assess the effect of MBs on demographic, clinical, genetic, CSF and neuroimaging AD biomarkers, and cognitive tests.ResultsWe included 247 participants with DS along the AD continuum, and 167 euploid controls (Table 1). The proportion of MB+ was higher in DS participants than controls, and progressively increased with AD clinical stages and age in the DS group (Fig. 1). By contrast, MB status did not differ by APOEε4 status, intellectual disability, or sex. In DS, the MB+ group had increased white matter hyperintensity volume and decreased hippocampal volumes compared to MB‐. DS MB+ also had lower CSF Aβ42/40 ratio, higher CSF t‐tau and p‐tau‐181 concentrations, and tended toward worse cognitive performance. Most effects gradually evolved with MB severity, although significant differences were mainly between MB‐ vs MB 2+ (Fig. 2). Finally, sensitivity analyses showed that most results became non‐significant when comparing DS MB+ to a 1:1 DS MB‐ group matched for age, sex, intellectual disability and/or AD diagnosis.ConclusionMB presence and severity are increased in adults with DS, and worsen with clinical progression and AD pathology. Yet, matched group analyses suggest that MB have limited impact on cognition and neurodegeneration. These results provide better characterisation of the presence and effect of MB in a population with an ultra‐high risk of developing AD who could benefit from new disease modifying drugs.

Volume of the posterior and not anterior hippocampus predicts spatial memory performance in Alzheimer´s Disease

AbstractBackgroundHippocampal volume atrophy is one of the most acknowledged supportive biomarkers for Alzheimer´s disease (AD). However, how hippocampal volumes along the anterior‐posterior axis mediate memory in aging and dementia is still subject of ongoing research. This study aimed to investigate the difference in volume of the hippocampus and its anterior and posterior subregions in a healthy young and senior control group as well as in mild cognitive impairment (MCI) due to AD and its ability to predict performance in spatial memory tasks.MethodThirteen healthy young controls (24.9 ± 2.8 years), fourteen healthy senior controls (66.6 ± 8.2 years) as well as fifteen MCI patients (71.1 ± 6.0 years) with biomarkers typical for AD were included in this study. Subject‐specific manual tracing of hippocampi according to the EADC‐ADNI Harmonized Protocol for Hippocampal Segmentation (HarP) was performed on high‐resolution T1‐weighted MRI. Following the segmentation into anterior and posterior hippocampal parts, volumetry of anterior, posterior and total hippocampus was computed. Every subject performed a combined spatial memory and object recognition paradigm. Brain‐behavior correlations were conducted in order to explore prediction of spatial memory performance.ResultVolume measures of the hippocampi significantly differentiated healthy from pathological aging. Larger volume of posterior hippocampus in MCI patients correlated with higher performance scores in the spatial memory task (mean percentage of successfully retrieved object locations) whereas total and anterior hippocampal volume did not predict performance in the spatial memory task.ConclusionThese results support a functional hippocampal anterior‐posterior differentiation in memory processes and further stress the importance of integrating the dissociation of the hippocampus along its long‐axis concerning functional connectivity in memory processes and dementia.

Reproducibility of Computerized Cephalometric Analysis Software Compared with Conventional Manual Tracing for Analyzing Skeletal Stability After Orthognathic Surgery.

This study aimed to compare the difference between analyzing skeletal stability after orthognathic surgery by lateral cephalogram measurement created from Dolphin software (version 11.95) compared with the manual technique. Twenty-eight patients who underwent mandibular setback surgery (BSSRO) were randomly selected between 2015 and 2021. Serial lateral cephalograms were analyzed at four different time sets postoperatively, and a total of 112 cephalometric radiographs were obtained. Horizontal measurement (BX), vertical measurement (BY), and 3 angular measurements (SNB, ANB, and Gonial angle) were analyzed by manual tracing and Dolphin software by 2 examiners. The intraclass correlation coefficient determined the intra-rater reliability. Parameter differences between timelines were observed for skeletal stability, and mean values between methods were compared using the Student's t-test. Both examiners were generally consistent in the repeated measurements (ICCs of the manual method ranged from 0.926 to 0.994, and the digital method ranged from 0.719 to 0.956). All variables represented skeletal stability at T0-T1, T0-T2, and T0-T3 showed no statistically significant differences between methods except ANB (T0-T1; p value = 0.009). Computerized cephalometric analysis software is relatively reproducible for assessing skeletal changes after orthognathic surgery and can be used routinely in follow-up.

Use of a digital contact tracing system in Singapore to mitigate COVID-19 spread

BackgroundContact tracing has been essential to reducing spread of COVID-19. Singapore leveraged technology to assist with contact tracing efforts using a Bluetooth-based app and token platform called ‘TraceTogether’.MethodsWe reviewed the impact of this system during the country’s Delta and Omicron waves (24 August 2021 to 17 February 2022) to identify differences in number of close contacts and time savings between full automation using TraceTogether alone as compared to manual contact tracing supplemented by TraceTogether. Characteristics of digital contact tracing app or token users were reviewed. Thereafter, the number of close contacts identified by manual and digital contact tracing methods, and the number of confirmed COVID-19 cases among contacts were analysed. The difference in time taken for identification of close contacts was also determined.FindingsAdoption rate for TraceTogether was high, with 93.3% of cases having a registered device. There was a 9.8 h (34.9%) reduction in time savings for close contacts to be informed using TraceTogether alone compared to manual contact tracing supplemented by TraceTogether. The proportion of close contacts automatically identified through TraceTogether alone and turned positive was 3.6%. For those identified through manual contact tracing supplemented by TraceTogether, this proportion was 12.5% and 6.2% for those served quarantine orders and health risk warnings respectively.InterpretationThe high adoption rate of ‘TraceTogether’ suggest that digital solutions remain a promising option to improve contact tracing in future epidemics. This may have been through its concurrent use with vaccine differentiated public health measures and policies which engender public trust. There is future potential for utilising such technology in managing communicable diseases to achieve good public health outcomes.

The effect of atrial morphology on atrial function in hypertrophic cardiomyopathy

Abstract Background Atrial dysfunction is a relevant marker of disease progression in hypertrophic cardiomyopathy (HCM). Left atrial (LA) longitudinal functional parameters and volumes derived from cardiac magnetic resonance (CMR) are associated with new-onset atrial fibrillation (AF) and its complications. The asymmetrical shape of the LA and its morphological alterations may influence LA function and could provide new mechanistic insights into HCM. Objective To assess the association between CMR derived functional and morphological LA characteristics in HCM patients. Methods Patients with an established diagnosis of HCM were included. HCM patients with a history of AF or without available CMR were excluded. CMR acquisitions used for clinical indication of heart failure diagnosis and etiology were retrospectively analyzed to obtain LA reservoir (LAres), booster (LAboost), and conduit (LAcond) strain on 2-chamber views (Medis Suite v3.2). Strain measurements of the other cardiac chambers were also performed. Manual tracing of LA endocardial border at the LA orthogonal longitudinal and transverse diameters on 2-, 3-, and 4-chamber views was performed (Philips Portal v10.1). Univariable and multivariable forward linear regression was employed to assess associations between functional and morphological LA measures. Results A total of 81 HCM patients were included, mean age 52±16 years, 59 (73%) male, and left ventricular ejection fraction 72.0±8.3%. Mean LAres was 27.2±11.0%, LAboost -14.9%±6.4%, and LAcond -12.5%±7.5%. LA morphological parameters are summarized in Table 1. The strongest correlates in multivariable regression for LAres were longitudinal LA diameter on 2-chamber view (Figure 1) and transverse LA diameter ratio between 4- and 2-chamber view (R2 0.30, p&amp;lt;0.001). The strongest correlates in multivariable regression for LAboost were longitudinal LA diameter on 2- and 4-chamber view, antero-posterior LA diameter on 3-chamber view, and LA area on 4-chamber view (R2 0.29, p&amp;lt;0.001). Longitudinal 2-chamber LA diameter also correlated with LAcond, but the strongest correlates of LAcond in multivariable regression were longitudinal-transversal LA diameter ratio on 2-chamber view, LA area on 3-chamber view, and LA sphericity (R2 0.25, p&amp;lt;0.001). Conclusions CMR-derived LA functions in HCM patients differently correlated with LA morphology. LA longitudinal 2-chamber diameter had a strong influence on all LA functions.Left atrial measures in HCMLA function and diameter correlations

The value of manual backward contact tracing to control COVID-19 in practice, the Netherlands, February to March 2021: a pilot study.

BackgroundContact tracing has been a key component of COVID-19 outbreak control. Backward contact tracing (BCT) aims to trace the source that infected the index case and, thereafter, the cases infected by the source. Modelling studies have suggested BCT will substantially reduce SARS-CoV-2 transmission in addition to forward contact tracing.AimTo assess the feasibility and impact of adding BCT in practice.MethodsWe identified COVID-19 cases who were already registered in the electronic database between 19 February and 10 March 2021 for routine contact tracing at the Public Health Service (PHS) of Rotterdam-Rijnmond, the Netherlands (pop. 1.3 million). We investigated if, through a structured questionnaire by dedicated contact tracers, we could trace additional sources and cases infected by these sources. Potential sources identified by the index were approached to trace the source's contacts. We evaluated the number of source contacts that could be additionally quarantined.ResultsOf 7,448 COVID-19 cases interviewed in the study period, 47% (n = 3,497) indicated a source that was already registered as a case in the PHS electronic database. A potential, not yet registered source was traced in 13% (n = 979). Backward contact tracing was possible in 62 of 979 cases, from whom an additional 133 potential sources were traced, and four were eligible for tracing of source contacts. Two additional contacts traced had to stay in quarantine for 1 day. No new COVID-19 cases were confirmed.ConclusionsThe addition of manual BCT to control the COVID-19 pandemic did not provide added value in our study setting.

Automated mapping of bedrock-fracture traces from UAV-acquired images using U-Net convolutional neural networks

This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from 0.55 cm spatial resolution aerial photographs, acquired by unmanned aerial vehicles (UAV), over the Wiborg Rapakivi granite outcrops in the islands off the coast of the Loviisa Region in Southern Finland. The workflow comprised training a U-Net CNN using a small subset of photographs with manually traced fractures for optimizing the network parameters using the root mean squared propagation optimizer and sigmoidal focal loss function for semantic segmentation of input images and pixel-wise identification of fracture traces. The ridge detection algorithm was then applied to the U-Net prediction results, followed by vectorization of the fracture-traces pixels as vector polylines representing the traces of fractures. Both intensity values of the pixels and topological connectivity were used in the process of vectorization. Quantitatively the results were assessed using various accuracy assessment metrics. Qualitative evaluations of the results were implemented by comparisons of orientations and length-frequency distributions of automatically- and manually-mapped traces.The results show that the model has the class-balanced accuracy score 0.945, predicting 88.79% of the fracture-trace pixels. Bedrock outcrops with well-exposed surfaces present high true positive rates (>99%). The demonstration (test) site has the class-balanced accuracy score of 0.873 and 75% true positive rate. Additionally, the fracture trace networks replicate the orientation distributions of the manually digitized traces. The length distributions of automatic traces, however, differ with varying intensity from the manual trace length distributions. However, this study demonstrates that the workflow can be successfully applied to UAV-acquired images for fast and efficient automated mapping of bedrock-fracture traces during the initial phases of structural characterization of a region.

Basal sauropodomorph locomotion: ichnological lessons from the Late Triassic trackways of bipeds and quadrupeds (Elliot Formation, main Karoo Basin).

Using modern ichnological and stratigraphic tools, we reinvestigate two iconic sauropodomorph-attributed tetradactyl ichnogenera, Pseudotetrasauropus and Tetrasauropus, and their stratigraphic occurrences in the middle Upper Triassic of Lesotho. These tracks have been reaffirmed and are stratigraphically well-constrained to the lower Elliot Formation (Stormberg Group, Karoo Basin) with a maximum depositional age range of <219-209 Ma (Norian). This represents the earliest record of basal sauropodomorph trackways in Gondwana, if not globally. Track and trackway morphology, the sedimentary context of the tracks, and unique features (e.g., drag traces) have enabled us to discuss the likely limb postures and gaits of the trackmakers. Pseudotetrasauropus has bipedal (P. bipedoida) and quadrupedal (P. jaquesi) trackway states, with the oldest quadrupedal Pseudotetrasauropus track and trackway parameters suggestive of a columnar, graviportal limb posture in the trackmaker. Moreover, an irregularity in the intermanus distance and manus orientation and morphology, in combination with drag traces, is indicative of a non-uniform locomotory suite or facultative quadrupedality. Contrastingly, Tetrasauropus, the youngest trackway, has distinctive medially deflected, robust pedal and manual claw traces and a wide and uniform intermanus distance relative to the interpedal. These traits suggest a quadrupedal trackmaker with clawed and fleshy feet and forelimbs held in a wide, flexed posture. Altogether, these trackways pinpoint the start of the southern African ichnological record of basal sauropodomorphs with bipedal and quadrupedal locomotory habits to, at least, c. 215 Ma in the middle Late Triassic.

Autologous Transplantation Tooth Guide Design Based on Deep Learning

BackgroundAutogenous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient CBCT scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated. PurposeWe aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline. Study design, setting, sampleThis retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest (ROI) images were extracted from 79 CBCT images. Predictor variableAutologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based Main outcome variableThe main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation (SD). The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline. AnalysesData were analyzed using the Kruskal–Wallis test. Statistical significance was set at p < 0.05. ResultsIn the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335±0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (p=0.688), and the SD values did not differ among the three groups (p=0.103). The deep learning-based design pipeline (0.017±0.001 min) provided a faster assessment compared to the manual design pipeline by both senior (19.676±2.386 min) and junior doctors (30.613±6.571 min) (p<0.001). Conclusions and RelevanceThe deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

The Dependency of Cochlear Lateral Wall Measurements on Observer and Imaging Type.

Assessment techniques for the cochlear spatial lateral wall are associated with inter-rater variability, but derived clinical recommendations nonetheless offer value for individualized electrode selection. Anatomical variations influence the location of cochlear implant electrodes inside the cochlea. Preoperative planning allows individualization of the electrode based on characterization of the bony lateral wall. The study used publicly available digitized temporal bones based on microslicing and computed tomography. Four experienced observers assessed the lateral wall applying manual tracing, linear regression scaling and elliptic-circular approximation methods in all modalities. Radial and height differences were computed in 90-degree steps from the round window center to the apex. Total length, total angular length, and tonotopic frequencies were computed for each reconstruction. Differences were found most pronounced between assessment methods in vertical direction across observers and imaging modalities. One of the five anatomies was consistently found to be of shorter cochlear duct length with estimation techniques yielding more conservative results compared with manual tracings. Assessment techniques for the bony lateral wall yield method, observer, and image modality related deviations. Automation of the anatomical characterization may offer potential in minimizing inaccuracies. Nonetheless, observers were consistently able to detect a smaller inner ear demonstrating the ability of current methods to contribute to an optimized choice of electrodes based on individual patient anatomy.

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers.

Hippocampal morphology in Huntington's disease, implications for plasticity and pathogenesis: The IMAGE-HD study

While striatal changes in Huntington's Disease (HD) are well established, few studies have investigated changes in the hippocampus, a key neuronal hub. Using MRI scans obtained from the IMAGE-HD study, hippocampi were manually traced and then analysed with the Spherical Harmonic Point Distribution Method (SPHARM-PDM) in 36 individuals with presymptomatic-HD, 37 with early symptomatic-HD, and 36 healthy matched controls. There were no significant differences in overall hippocampal volume between groups. Interestingly we found decreased bilateral hippocampal volume in people with symptomatic-HD who took selective serotonin reuptake inhibitors compared to those who did not, despite no significant differences in anxiety, depressive symptoms, or motor incapacity between the two groups. In symptomatic-HD, there was also significant shape deflation in the right hippocampal head, showing the utility of using manual tracing and SPHARM-PDM to characterise subtle shape changes which may be missed by other methods. This study confirms previous findings of the lack of hippocampal volumetric differentiation in presymptomatic-HD and symptomatic-HD compared to controls. We also find novel shape and volume findings in those with symptomatic-HD, especially in relation to decreased hippocampal volume in those treated with SSRIs.

An open-source digital contact tracing system tailored to haulage.

Digital contact tracing presents numerous advantages compared to manual contact tracing methods, especially in terms of enhanced speed and automation. Nevertheless, a lack of comprehensive evaluation regarding functionality, efficiency, benefits, and acceptance within communities remains. Here we primarily focus on the functionality of THEA-GS, an open-source digital contact tracing tool developed through consultation with stakeholders. Additionally, we provide insights from its implementation on a limited sample of haulage drivers in Uganda, serving as a representative case for a low- and middle-income country. THEA-GS comprises two primary components: (a) a smartphone application, and (b) a suite of server-programs responsible for data processing and analysis, including databases and a web-based interface featuring dashboards. In essence, the mobile application records the timestamped location of haulage drivers within the road network and identifies possible transmission hotspots by analyzing factors such as the duration of stops and the communities associated with them. The tool can be integrated with national infrastructure to compare drivers' diagnostic results and contact structure, thereby generating individual and community risk assessments relative to the road network. During the Omicron-variant wave of the COVID-19 pandemic, a total of 3,270 haulage drivers were enrolled between October 2021 and October 2022. Around 75% of these drivers utilized THEA-GS for approximately two months. Based on an analysis of 3,800 test results, which included 48 positive cases, 125 contacts, and 40 million time-stamped GPS points, THEA-GS shows a significant speed improvement, being approximately 90 times faster than MCT. For instance, the average time from sample collection to notifying a case and their contacts was approximately 70 and 80 min, respectively. The adoption of this tool encountered challenges, mainly due to drivers' awareness of its purpose and benefits for public health. THEA-GS is a place-based digital contact tracing tool specifically designed to assist National Public Health Institutions in managing infectious disease outbreaks involving the haulage industry as a high-risk group. While its utility, acceptance, and accuracy have not been fully evaluated, our preliminary tests conducted in Uganda indicate the tool's functionality is robust, but social acceptance and adoption are heavily reliant on establishing trust among users.

Crown root ratio and angulation of various permanent teeth in patients with and without cleft lip/palate

ABSTRACT Aims and Objectives: The purpose of this study was to analyze parameters of tooth in subjects with nonsyndromic cleft lip and palate (CLCP) and to compare the findings with the control groups. Teeth in the cleft-affected and nonaffected side in the same unilateral cleft lip and palate (ULCP) patient were also compared. Materials and Methods: The study included 200 subjects in the age group of 12–37 years who were divided into two groups: nonsyndromic CLCP and control group. The control group was further subdivided into skeletal Class I, II, and III subgroups based on ANB, AO-BO, and overjet, respectively. Crown height, root length, total tooth length, crown root ratio (C/R ratio), and tooth angulations of maxillary permanent central incisors, canines, 1st premolars, and 1st molar were measured on orthopantomograms using both ITK SNAP software and manual tracing. Results: Crown heights significantly increased while root lengths significantly decreased in the CLCP patients when compared to control groups. Total tooth lengths of central incisor, canine, and 1st premolar in CLCP patients were found to be decreased significantly. C/R ratios of all the teeth of nonsyndromic CLCP group irrespective of the cleft affected or nonaffected side was found to be increased as compared to the control groups. There was a significant difference seen in crown height, root length and mesiodistal tooth angulations in the cleft affected side when compared to the non affected side in a UCLCP patient’s dentition. However the total tooth length and the C/R ratio were not affected. Conclusion: Common findings of CLCP are aberrant teeth development, altered tooth morphology, and malformed teeth. The severity of cleft has an impact on the tooth development and morphology as it was observed that delayed root development and short roots, decreased total tooth length, increased C/R ratio and variations in teeth angulations in CLCP patients were affected by the presence and severity of cleft.

Hits and Misses: Digital Contact Tracing in a Pandemic

Traditional contact tracing is one of the most powerful weapons people have in the battle against a pandemic, especially when vaccines do not yet exist or do not afford complete protection from infection. But the effectiveness of contact tracing hinges on its ability to find infected people quickly and obtain accurate information from them. Therefore, contact tracing inherits the challenges associated with the fallibilities of memory. Against this backdrop, digital contact tracing is the “dream scenario”—an unobtrusive, vigilant, and accurate recorder of danger that should outperform manual contact tracing on every dimension. There is reason to celebrate the success of digital contact tracing. Indeed, epidemiologists report that digital contact tracing probably reduced the incidence of COVID-19 cases by at least 25% in many countries, a feat that would have been hard to match with its manual counterpart. Yet there is also reason to speculate that digital contact tracing delivered on only a fraction of its potential because it almost completely ignored the relevant psychological science. We discuss the strengths and weaknesses of digital contact tracing, its hits and misses in the COVID-19 pandemic, and its need to be integrated with the science of human behavior.

Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review.

Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images.

Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images. Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%-81%), while, with the bull's eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved. DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.