Conventional Visual Assessment Research Articles

High-resolution UAV-based blueberry scorch virus mapping utilizing a deep vision transformer algorithm

Blueberry scorch virus (BIScV), transmitted by aphids, causes a serious disease in highbush blueberries with a significant economic impact. Early detection and mapping of the distribution of BIScV infected plants in fields are critical to implementing effective disease management practices, such as the timely removal of infected bushes and control of aphid vectors. The conventional visual plant assessment for symptoms remains dominant in BIScV detections, though it is labor-intensive, time-consuming, and costly. In recent years, the use of remote sensing techniques has become popular for in-field assessments of crop diseases and insect pests incidence, and thus provides an effective approach for detecting and mapping BIScV infections. Convolutional Neural Networks (CNNs) are among the most widely employed algorithms in remote sensing image classification. However, CNNs have some limitations in their ability to obtain global information dependency due to the convolution’s constrained receptive field in each layer. To address this challenge, the self-attention mechanism utilized in Vision Transformers (ViTs) was suggested in previous studies for achieving flexible global information dependency through facilitating communication among arbitrary pixels in images. As such, we developed a CNN-ViT-based deep learning algorithm (named “Scorch Mapper”), a pixel-based classifier, that utilizes both the functionality and capabilities of CNNs in capturing local visual characteristics and ViTs for acquiring long-range information dependency for the mapping of BIScV. We also compared the developed Scorch Mapper to several other CNN– and ViT-based algorithms, including a 2D CNN, ResNet, HybridSN, Swin Transformer, Efficient Net, CMT, InFormer, and Efficient Former. Our results demonstrated the superiority of the Scorch Mapper compared to other CNN– and ViT-based algorithms. Research findings also show that the Scorch Mapper is effective and can be applied over a wide area to support BIScV mapping and monitoring. Furthermore, the developed model opens a new window for future automatic BIScV mapping utilizing cutting-edge remote sensing algorithms and technologies.

Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning.

Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD.

Occlusal caries detection and monitoring using a 3D intraoral scanner system. An in vivo assessment

ObjectiveTo assess the agreement in detecting and monitoring occlusal caries over thirty months using conventional visual and radiographic assessment and an intraoral scanner system which supports automated caries scoring. MethodsNinety-one young participants aged 12–19 years were included in the study. All occlusal surfaces were examined visually, radiographically (when indicated), and scanned with the TRIOS 4 intraoral scanner. TRIOS Patient Monitoring software (vers. 2.3, 3Shape TRIOS A/S, Denmark) was used for automated caries detection on the 3D digital models. ResultsFifty-five of the study participants were re-examined after 30-months. Significant differences regarding caries detection were found between the conventional methods and the automated caries scoring system (p < 0.01), with moderate positive percent agreement (49–61%) and high negative percent agreement (87–98%). All methods reported significant caries progression over the follow-up period (p < 0.01). However, the automated system showed significantly more caries progression than the other methods (p < 0.01). ConclusionsThe software for automated caries detection and classification showed moderate positive agreement and strong negative agreement with the conventional methods considering both the baseline and the follow-up assessments. The automated caries scoring system detected significantly fewer caries lesions and tended to underestimate the caries severity. All methods indicated significant caries progression over the follow-up period, while the automated system detected more caries progression. Clinical significanceThe TRIOS system supporting automated occlusal caries detection and classification can assist in detecting and monitoring occlusal caries on permanent teeth as a complementary tool to the conventional methods. However, the operator should be aware that the automated system shows a tendency to underestimate the caries presence and lesion severity.

Application of Transfer Learning in Smart Agriculture to Combat Black Rot Bacteria

With diseases and weeds alone accounting for approximately 30% of productivity losses in agriculture worldwide, plant diseases are a significant factor. Farmers frequently consult experts to diagnose their crops in an effort to reduce these losses. On the other hand, conventional visual assessment techniques focused on elements like leaf color, shape, and texture can be inefficient, time-consuming, and expensive. This study suggests using symptom traits like yellow leaves, black lesions, wilting, and plant mortality in crops like apple and grape to anticipate plant diseases, notably black rot. AlexNet, VGGNet, ResNet, and Inception are the four deep learning models examined in the study; VGGNet achieved the greatest accuracy of 98.19%.

Formulating a Novel Physical Equation to Quantify Interocular Visual Acuity Influence

ABSTRACT The limitation of conventional visual acuity assessment, which primarily focuses on individual eye performance (monocular visual acuity tests). This study addresses this limitation by emphasizing the importance of binocular vision, where both eyes work together. Binocular vision provides numerous advantages, such as improved depth perception, a wider field of view, reduced visual fatigue, and binocular summation. The article introduces a new mathematical model based on the physics of constructive interference in light waves and the anatomy of visual nerves. This model yields an equation that quantifies how weakness in one eye affects the visual acuity of the other. By analyzing the correlation between the rates of visual weakness in each eye and the corresponding distances, utilizing the Snellen 6-meter test, valuable insights are gained into the complex interplay between eye deficiencies and their collective impact on overall visual acuity. The equations derived from this study can have significant implications for eye care professionals, clinicians, and patients, contributing to a more comprehensive understanding of visual impairments and enriching the field of optics and vision science.

Diagnostic Performance of Multispectral SWIR Transillumination and Reflectance Imaging for Caries Detection.

The aim of this clinical study was to compare the diagnostic performance of dual short wavelength infrared (SWIR) occlusal transillumination and reflectance multispectral imaging with conventional visual assessment and radiography for caries detection on premolars scheduled for extraction for orthodontics reasons. Polarized light microscopy (PLM) and micro-computed tomography (microCT) performed after tooth extraction were used as gold standards. The custom-fabricated imaging probe was 3D-printed and the imaging system employed a SWIR camera and fiber-optic light sources emitting light at 1300 nm for occlusal transillumination and 1600 nm for reflectance measurements. Teeth (n = 135) on 40 test subjects were imaged in vivo using the SWIR imaging prototype in the study and teeth were extracted after imaging. Our study demonstrates for the first time that near-simultaneous real-time transillumination and reflectance video can be successfully acquired for caries detection. Both SWIR imaging modalities had markedly higher sensitivity for lesions on proximal and occlusal surfaces compared to conventional methods (visual and radiographic). Reflectance imaging at 1600 nm had higher sensitivity and specificity than transillumination at 1300 nm. The combined SWIR methods yielded higher specificity but the combined sensitivity was lower than for each individual method.

Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

Disease Assessments in Patients with Glioblastoma.

The neuro-oncology team faces a unique challenge when assessing treatment response in patients diagnosed with glioblastoma. Magnetic resonance imaging (MRI) remains the standard imaging modality for measuring therapeutic response in both clinical practice and clinical trials. However, even for the neuroradiologist, MRI interpretations are not straightforward because of tumor heterogeneity, as evidenced by varying degrees of enhancement, infiltrating tumor patterns, cellular densities, and vasogenic edema. The situation is even more perplexing following therapy since treatment-related changes can mimic viable tumor. Additionally, antiangiogenic therapies can dramatically decrease contrast enhancement giving the false impression of decreasing tumor burden. Over the past few decades, several approaches have emerged to augment and improve visual interpretation of glioblastoma response to therapeutics. Herein, we summarize the state of the art for evaluating the response of glioblastoma to standard therapies and investigational agents as well as challenges and future directions for assessing treatment response in neuro-oncology. Monitoring glioblastoma responses to standard therapy and novel agents has been fraught with many challenges and limitations over the past decade. Excitingly, new promising methods are emerging to help address these challenges. Recently, the Response Assessment in Neuro-Oncology (RANO) working group proposed an updated response criteria (RANO 2.0) for the evaluation of all grades of glial tumors regardless of IDH status or therapies being evaluated. In addition, advanced neuroimaging techniques, such as histogram analysis, parametric response maps, morphometric segmentation, radio pharmacodynamics approaches, and the integrating of amino acid radiotracers in the tumor evaluation algorithm may help resolve equivocal lesion interpretations without operative intervention. Moreover, the introduction of other techniques, such as liquid biopsy and artificial intelligence could complement conventional visual assessment of glioblastoma response to therapies. Neuro-oncology has evolved over the past decade and has achieved significant milestones, including the establishment of new standards of care, emerging therapeutic options, and novel clinical, translational, and basic research. More recently, the integration of histopathology with molecular features for tumor classification has marked an important paradigm shift in brain tumor diagnosis. In a similar manner, treatment response monitoring in neuro-oncology has made considerable progress. While most techniques are still in their inception, there is an emerging body of evidence for clinical application. Further research will be critically important for the development of impactful breakthroughs in this area of the field.

Identification of olive leaf disease through optimized deep learning approach

The production of olives in Saudi Arabia, which accounts for around 6% of worldwide output, is regarded as one of the best in the world. Because olive trees are rain-fed and produced using conventional methods, yields vary greatly each year, which is made worse by viral illnesses and climate change. Therefore, it is necessary to identify plant illnesses early on. Farmers diagnose plant illnesses using conventional visual assessment or laboratory analysis. Diagnosing illnesses affecting olive leaves has been improved with deep learning (DL). To identify and categorize plant illnesses, this research introduces an Optimized Artificial Neural Network (ANN) that analyses the plant's leaf. Data is first integrated for preprocessing, relevant features are extracted, and the Whale Optimization Algorithm (WOA) is used to select necessary features. Then the data is classified using ANN. The ANN classification approach utilizes the feed-forward neural network method (FFNN). ANN is a highly adaptable technology being utilized widely to address various problems. This study applies categorization to exclude possibilities throughout each stage, improving prediction accuracy. Compared to the current model employed for plant disease detection, the suggested model showed a considerable performance increase in Precision, Recall, Accuracy, and F1-measure.

Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.

Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

ObjectiveTo evaluate the diagnostic performance of artificial intelligence (AI)–based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20‐min) electroencephalography (EEG) recordings.MethodsWe evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED definition to assess the automated detections grouped into clusters by the algorithms. We used three previously developed AI algorithms: Encevis, SpikeNet, and Persyst. The diagnostic gold standard (epilepsy or not) was derived from video‐EEG recordings of patients' habitual clinical episodes. We compared the algorithms with the gold standard at the recording level (epileptic or not). The independent validation data set (not used for training) consisted of 20‐min EEG recordings containing sharp transients (epileptiform or not) from 60 patients: 30 with epilepsy (with a total of 340 IEDs) and 30 with nonepileptic paroxysmal events. We compared sensitivity, specificity, overall accuracy, and the review time‐burden of the fully automated and hybrid approaches, with the conventional visual assessment of the whole recordings, based solely on unrestricted expert opinion.ResultsFor all three AI algorithms, the specificity of the fully automated approach was too low for clinical implementation (16.67%; 63.33%; 3.33%), despite the high sensitivity (96.67%; 66.67%; 100.00%). Using the hybrid approach significantly increased the specificity (93.33%; 96.67%; 96.67%) with good sensitivity (93.33%; 56.67%; 76.67%). The overall accuracy of the hybrid methods (93.33%; 76.67%; 86.67%) was similar to the conventional visual assessment of the whole recordings (83.33%; 95% confidence interval [CI]: 71.48–91.70%; p > .5), yet the time‐burden of review was significantly lower (p < .001).SignificanceThe hybrid approach, where human raters apply the operational IED criteria to automated detections of AI‐based algorithms, has high specificity, good sensitivity, and overall accuracy similar to conventional EEG reading, with a significantly lower time‐burden. The hybrid approach is accurate and suitable for clinical implementation.

Clinical and Prognostic Impact From Objective Analysis of Post-Angioplasty Fractional FlowReserve Pullback.

This study sought to evaluate clinical implications of the residual fractional flow reserve (FFR) gradient after angiographically successful percutaneous coronary intervention (PCI). Recent studies have demonstrated FFR measured after PCI is associated with clinical outcome after PCI. Although post-PCI FFR pull back tracings provide clinically relevant information on the residual FFR gradient, there are no objective criteria for assessing post-PCI FFR pull back tracings. A total of 492 patients who underwent angiographically successful PCI and post-PCI FFR measurement with pull back tracings were analyzed. The presence of the major residual FFR gradient after PCI was assessed by both conventional visual interpretation of the pull back tracings and objective analysis using the instantaneous FFR gradient per unit time (dFFR(t)/dt) with a cutoff value of dFFR(t)/dt≥0.035. Classification agreement between 2 independent operators for the presence of the major residual FFR gradient was compared before and after providing dFFR(t)/dt results. Target vessel failure (TVF), a composite of cardiac death, target vessel myocardial infarction, or clinically driven target vessel revascularization at 2 years, was compared according to the presence of the major residual FFR gradient. Among the study population, 33.9% had the major residual FFR gradient defined by dFFR(t)/dt. The classification agreement between operators' assessments for the major residual FFR gradient increased with dFFR(t)/dt results compared with conventional visual assessment (Cohen's kappa=0.633 to 0.819; P< 0.001; intraclass correlation coefficient: 0.776 to 0.901; P< 0.001). Patients with major residual FFR gradient were associated with a higher risk of TVF at 2 years than those without major residual FFR gradient (9.0% vs 2.2%; P< 0.001). Inclusion of the major residual FFR gradient to a clinical prediction model significantly increased discrimination and reclassification ability (C-index=0.539 vs 0.771; P = 0.006; net reclassification improvement=0.668; P = 0.007; integrated discrimination improvement =0.033; P = 0.017) for TVF at 2 years. The presence of the major residual FFR gradient was independently associated with TVF at 2 years, regardless of post-PCI FFR or percent FFR increase (adjusted hazard ratio: 3.930; 95% confidence interval: 1.353-11.420; P = 0.012). Objective analysis of post-PCI FFR pull back tracings using dFFR(t)/dt improved classification agreement on the presence of the major residual FFR gradient among operators. Presence of the major residual FFR gradient defined by dFFR(t)/dt after angiographically successful PCI was independently associated with an increased risk of TVF at 2 years. (Automated Algorithm Detecting Physiologic Major Stenosis and Its Relationship with Post-PCI Clinical Outcomes [Algorithm-PCI]; NCT04304677; Influence of FFR on the Clinical Outcome After Percutaneous Coronary Intervention [COE-PERSPECTIVE]; NCT01873560).

99mTc-white blood cell SPECT/CT to assess diabetic foot osteomyelitis remission: contribution of semi-quantitative scoring system.

We previously reported that 99mTc-White blood cell (WBC) single-photon emission computed tomography (SPECT/CT) could be a useful tool to assess diabetic foot osteomyelitis (DFO) remission and guide the duration of antibiotic treatment. The aim of the present study was to evaluate the performance and reproducibility of two analysis methods to diagnose DFO remission using 99mTc-WBC-SPECT/CT. 99mTc-WBC-SPECT/CT performed for patients with DFO at the end of antibiotic treatment were retrospectively read by two nuclear physicians (one senior and one junior). Assessment by conventional visual analysis and by the use of a semi-quantitative scoring system, the composite score index (CSI), was performed. The performance and reproducibility of methods were compared between the two nuclear physicians. Successful treatment of DFO was defined by the absence of DFO relapse at the same site within 1 year. A total of 68 patients with 74 DFO were included. Three were excluded from the analysis due to the low quality of SPECT/CT; among the 71 DFO analyzed, 11 (15.5%) had a relapse during follow-up. Performances of 99mTc-WBC-SPECT/CT to predict DFO remission with conventional visual assessment were significantly lower for junior than for senior nuclear physician with moderate inter-rater agreement (Kappa: 0.417). Performances with the use of CSI were similar between the two readers with good inter-rater agreement (Kappa: 0.756). The study found that conventional visual assessment of 99mTc-WBC-SPECT/CT to assess DFO remission requires experience, and supported that CSI could be useful for junior nuclear physician to discriminate residual infections and inflammatory post-treatment uptake.

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases

Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clinically relevant issue is the selection of high-risk subjects who need active surveillance among equivocal cases. We validated the clinical feasibility of DL compared with visual rating or quantitative measurement for assessing the diagnosis and prognosis of subjects with equivocal amyloid scans. 18F-florbetaben scans of 430 cases (85 NC, 233 mild cognitive impairment, and 112 AD) were assessed through visual rating-based, quantification-based, and DL-based methods. DL was trained using 280 two-dimensional PET images (80%) and tested by randomly assigning the remaining (70 cases, 20%) cases and a clinical validation set of 54 equivocal cases. In the equivocal cases, we assessed the agreement among the visual rating, quantification, and DL and compared the clinical outcome according to each modality-based amyloid status. The visual reading was positive in 175 cases, equivocal in 54 cases, and negative in 201 cases. The composite SUVR cutoff value was 1.32 (AUC 0.99). The subject-level performance of DL using the test set was 100%. Among the 54 equivocal cases, 37 cases were classified as positive (Eq(deep+)) by DL, 40 cases were classified by a second-round visual assessment, and 40 cases were classified by quantification. The DL- and quantification-based classifications showed good agreement (83%, κ = 0.59). The composite SUVRs differed between Eq(deep+) (1.47 [0.13]) and Eq(deep-) (1.29 [0.10]; P < 0.001). DL, but not the visual rating, showed a significant difference in the Mini-Mental Status Examination score change during the follow-up between Eq(deep+) (- 4.21 [0.57]) and Eq(deep-) (- 1.74 [0.76]; P = 0.023) (mean duration, 1.76 years). In visually equivocal scans, DL was more related to quantification than to visual assessment, and the negative cases selected by DL showed no decline in cognitive outcome. DL is useful for clinical diagnosis and prognosis assessment in subjects with visually equivocal amyloid scans.

The IACT Optical System of the TAIGA Observatory Complex

TAIGA (Tunka Advanced Instrument for Gamma Astronomy) is designed for studying gamma rays and charged cosmic particles in the energy range of 1013–1018 eV. The staff of the Joint Institute for Nuclear Research is now working on the design and fabrication of Cherenkov telescope elements (IACTs). The IACT field of view is ~10° × 10°, due to a Davis–Cotton optical layout with 34 mirrors 0.60 m in diameter, a focal length of 4.75 m, and 560 XP1911 PMT camera. The first IACT was commissioned in 2016 in the Tunka Valley. The second IАСТ is now being prepared for operation. The steps of PMT alignment and the results from its calibration are thoroughly described along with the fabrication of a mirror and its optical parameters. The technique for adjusting the mirror is presented as well. It replaces the conventional visual assessment of the image with pattern recognition software that is applied to a screen shot of the calibration source. This software ensures highly precise calculations of the mirror’s adjusting screws to obtain a correct image.

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat.

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

Estimation of Individual Tree Health Condition for Japanese Mountain Cherry (Cerasus jamasakura) Using Airborne LiDAR

This study examined the usefulness of airborne light detection and ranging (LiDAR) data for estimating the individual tree health condition in Japanese mountain cherry (Cerasus jamasakura) in Yoshinoyama, Nara Prefecture, Japan. LiDAR variables that represented the ratio of lasers hitting tree components were calculated and their effectiveness was examined by relating them to the results of conventional field-based visual tree health assessments based on ordination, correlation analyses, and generalized linear models. The results showed that many of the LiDAR variables had significant correlations with the variables derived from visually evaluated tree health condition. In particular, the proportion of “only” returns, which represents the ratio of the lasers reflected from the crown surfaces, was the most effective for estimating total health condition in relation to the crown density, one of the key health indicators for representing physical properties. The individuals with large estimation errors had smaller crowns than the individuals with small errors, suggesting that sufficiently large crown sizes are important for more accurate estimations of the tree health condition using airborne LiDAR data.

Estimating the number of contributors to DNA mixtures provides a novel tool for ecology

Abstract Mixtures of DNA from multiple contributors present a novel opportunity to count individuals to inform fish and wildlife ecology. We apply a likelihood‐based framework to estimate the number of contributors to a DNA mixture for ecological applications. We then assess the performance of DNA mixture estimation through a combination of simulation analyses, laboratory testing, and a field trial to estimate fish predation rates from stomach content analysis. Simulations indicated reasonably sized genetic marker panels could estimate the number of contributors to mixtures comprised of up to 10 individuals, with potential to resolve larger mixtures with additional markers. Mixture estimates demonstrated robustness to common genotyping errors associated with fish and wildlife genetics applications. Laboratory trials demonstrated that DNA combined from multiple yellow perch (Perca flavescens) could be successfully genotyped with a 14‐loci microsatellite panel and led to successful estimation for up to 5‐contributor mixtures. Stomach content analysis with DNA mixtures indicated a 5‐fold increase in estimated predation rates of yellow perch by largemouth bass (Micropterus salmoides) relative to conventional visual assessment of diet contents which can miss partially digested prey items. DNA mixtures have potential to expand applications of count‐based ecological analyses. Technical challenges in generating genotypes from DNA mixtures may initially limit their use, however, advances in next generation genotyping platforms are anticipated to surmount these obstacles. Chiefly, we envision opportunity for DNA mixtures to advance eDNA analysis beyond presence/absence based inference to enumeration of specimens.

The Role of Digital 3D Scanned Models in Dental Students' Self-Assessments in Preclinical Operative Dentistry.

The aim of this study was to determine how dental student self-assessment and faculty assessment of operative preparations compared for conventional visual assessment versus assessment of scanned digital 3D models. In 2016, all third-year students in the Class of 2018 (N=35) at Harvard School of Dental Medicine performed preclinical exams of Class II amalgam preparations (C2AP) and Class III composite preparations (C3CP) and completed self-assessment forms; in 2017, all third-year students in the Class of 2019 (N=34) performed the same exams. Afterwards, the prepared typodont teeth were digitally scanned. Students self-assessed their preparations digitally, and four faculty members graded the preparations conventionally and digitally. The results showed that, overall, the students assessed their preparations higher than the faculty assessments. The mean student-faculty gaps for C2AP and C3CP in the conventional assessments were 11% and 5%, respectively. The mean digital student-faculty gap for C2AP and C3CP were 8% and 2%, respectively. In the conventional assessments, preclinical performance was negatively correlated with the student-faculty gap (r=-0.47, p<0.001). The correlations were not statistically significant with the digital assessments (p=0.39, p=0.26). Students in the bottom quartile significantly improved their self-assessment accuracy using digital self-assessments over conventional assessments (C2AP 10% vs. 17% and C3CP 3% vs. 10%, respectively). These results suggest that digital assessments offered a significant learning opportunity for students to critically self-assess themselves in operative preclinical dentistry. The lower performing students benefitted the most, improving their assessment ability to the level of the rest of the class.

Computerised analysis of the antepartum cardiotocogram (CTG) for care of the compromised fetus

Fifty years ago the fetus was inaccessible to objective measurement. But the development of electronic fetal heart rate (FHR) monitoring and ultrasound imaging revolutionised fetal care. Continuous FHR monitoring was introduced and quickly implemented for intrapartum monitoring. Its ability to detect fetal distress before labour (antepartum CTG was also rapidly appreciated. But there were problems. The diagnosis of a terminal state is easy, but its prediction is more difficult. The unresolved issue is calibrating the grey zone between normality and the terminal trace. Conventional visual assessment is well known to be very unreliable, both inter- and intra-observer variations are unacceptably large. Computerised analysis has helped by standardising the interpretation and measurement of grey-zone features which allows a more evidence- based approach. The Dawes–Redman system of computerised analysis was first marketed in 1991, after nearly 15 years of development in the high risk pregnancy unit in Oxford. It is currently commercially available (Huntleigh Healthcare) and widely used in the UK, Europe, and the Middle and Far East. It is based on the Dawes–Redman criteria of normality which cover many aspects of the CTG including short term variation, fetal heart rate, accelerations, deceleration, fetal movements, fast sinusoidal patterns and the electronic quality of the trace. Some of the criteria are specific for gestational age. As would be expected the preterm fetus has different limits of normality from the term fetus. These criteria are comparable to the FIGO and NICE criteria, but more extensive, more sophisticated and based on better evidence. Application of the Dawes–Redman system abolishes the requirement for a trace of fixed duration. When all criteria of normality have been met, advice is given that the CTG can be stopped. This might be after 10 min or after 55 min. The average is about 16–18 min. If criteria are not met at 60 min the CTG is classified as abnormal (criteria are not met), the underlying features are highlighted and further investigation and management decisions are necessary. Overall the flexibility of the duration of monitoring saves time. The criteria are evidence based on more than 100,000 antepartum CTGs in the Oxford archive. In this meeting three aspects of the system will be presented: its pathophysiological basis (Redman), its clinical implementation (Stanger) and its use by midwives in a high risk unit (Albert).