Seed classification with random forest models.

Premise: To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods and Results: Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. Conclusions: This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.

Handloomed fabrics recognition with deep learning.

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned "gamucha" from India's northeast, from counterfeit powerloom imitations. Our study's objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures-VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201-were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven "gamucha"s and counterfeit powerloom imitations-a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products.

Dataset of ocean vessel traffic in the North Sea.

Automatic Identification System (AIS) is a technology that allows ships to broadcast their position, course, speed, and other information to other vessels or shore-based stations. By collecting and analysing this data, it is possible to create a heatmap of ship activity in a particular region, such as the North Sea. This heatmap acts as a representation of vessel activity per class. A heatmap in a standard geoinformatics format may be preferable to scientific researchers as it would quickly allow users to overlay their own data onto the vessel density layer thus providing spatial context and an ability to compare their dataset to the distribution and intensity of ship activity in a particular region. This dataset represents ocean vessel activity in the North Sea for 2022 and was created using AIS data collected using multiple coastal receivers. The dataset was created from reported vessel positions aggregated both spatially and temporally. The end goal of this data processing is to provide a publicly available spatial layer that can be queried to provide monthly vessel traffic statistics for a region in the North Sea. The data was spatially filtered to only include AIS messages for Latitudes between 49.5 and 53.8 degrees North, and 0.2 and 7 degrees East. The bounding box was chosen as it includes Belgium canals and the Belgium part of the North Sea. The dataset has multiple uses as a collaboration dataset, some example of use-cases that this dataset has been used for include using it asa time-series of statistical priors for vessel classes in order to improve vessel classification algorithms and to visualise vessel behaviour in order to locate potential mooring sites where the risk of potential fishing net snags is low. It has also been used to locate areas of potential anchor scarring in anchorages near ports.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics.

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Drug-related causes attributed to acute kidney injury and their documentation in intensive care patients.

PURPOSE: To investigate drug-related causes attributed to acute kidney injury (DAKI) and their documentation in patients admitted to the Intensive Care Unit (ICU). METHODS: This study was conducted in an academic hospital in the Netherlands by reusing electronic health record (EHR) data of adult ICU admissions between November 2015 to January 2020. First, ICU admissions with acute kidney injury (AKI) stage 2 or 3 were identified. Subsequently, three modes of DAKI documentation in EHR were examined: diagnosis codes (structured data), allergy module (semi-structured data), and clinical notes (unstructured data). RESULTS: n total 8124 ICU admissions were included, with 542 (6.7%) ICU admissions experiencing AKI stage 2 or 3. The ICU physicians deemed 102 of these AKI cases (18.8%) to be drug-related. These DAKI cases were all documented in the clinical notes (100%), one in allergy module (1%) and none via diagnosis codes. The clinical notes required the highest time investment to analyze. CONCLUSIONS: Drug-related causes comprise a substantial part of AKI in the ICU patients. However, current unstructured DAKI documentation practice via clinical notes hampers our ability to gain better insights about DAKI occurrence. Therefore, both automating DAKI identification from the clinical notes and increasing structured DAKI documentation should be encouraged.

Novel automated identification and quantification database using liquid chromatography quadrupole time-of-flight mass spectrometry for quick, comprehensive, cheap and extendable organic micro-pollutant analysis in environmental systems.

In order to protect human health and the environment, highly efficient, low-cost, labor-saving, and green analysis of toxic chemicals are urgently required. To achieve this objective, we have developed a novel database-based automated identification and quantification system (AIQS) using LC-QTOF-MS. Since the AIQS uses retention times (RTs), exact MS and MS-MS spectra, and calibration curves of 484 chemicals registered in the database instead of the use of standards, the targets can be determined with low-cost in a short time. The AIQS uses Sequential Window Acquisition of All Theoretical Fragment-ion Spectra as an acquisition method by which we can obtain accurate MS and MS-MS spectra of all detectable substances in a sample with minimal interference from co-eluted peaks. Identification is certainly done using RTs, mass error, ion ratios (a precursor to two product ions), and accurate MS and MS-MS spectra. Consequently, the chance of misidentification is very low even in dirty samples. To examine the accuracy of the AIQS, two collaborative tests were conducted. The first test used 208 pesticide standards at two concentrations (10 and 100 ng mL-1) using 7 instruments, and showed that average trueness was 106 and 95.2%, respectively, with relative standard deviations of 90% of the test compounds below 30%. The second collaborative study involved 5 laboratories carrying out recovery tests on 200 pesticides using 10 river waters. The average recovery was 71.6%; this was 15% lower than that using purified water probably due to the matrix effects. The average relative standard deviation was 30% worse than that of measurement of the standards. Both the recovery and reproducibility, however, satisfied the criteria of Analytical Method Validity Guidelines, Ministry of Health, Labour and Welfare, Japan. Instrument detection limits of 96% of the registered compounds are below 10 pg. The AIQS allows for easy addition of new substances and retrospective analysis after their addition. The results applied to actual samples showed that the AIQS has sufficient identification and quantification performance as a target screening method for a large number of substances in environmental samples.

Automated identification of innocent Still's murmur using a convolutional neural network.

Background: Still's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists. Objectives: To develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral. Methods: The study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm. Results: A comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943. Conclusions: Still's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

Review on Facial-Recognition-Based Applications in Disease Diagnosis.

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.

Evaluation of sulbactam and colistin/sulbactam efficacy against multiple resistant Acinetobacter baumannii blood isolates.

PURPOSE: We aimed to compare the results of the BD Phoenix (TM) M50 ID/AST system and the gold standard broth microdilution method. We also evaluated the potential of a new therapeutic combination (colistin/sulbactam) for colistin resistance among Acinetobacter baumanni strains. METHODS: Growth in blood samples was detected with the BACTEC (BD Becton Dickinson, ABD) continuous monitoring blood culture system. Strains were identified by Phoenix (BD Phoenix™ M50, ABD) automated bacterial identification system and antimicrobial susceptibility results were obtained. A total of 92 A. baumannii complex isolates showing resistance to at least three antibiotic classes were included in the study. Colistin susceptibility results (both susceptible and resistant strains) detected by the Phoenix device were confirmed by the reference method, the liquid microdilution method. The concentration index (FIC) was used to determine the efficacy of fractional inhibitor drug combinations, the efficacy of colistin/sulbactam combination against 50 multiresistant A. baumannii complex strains was investigated using the checkerboard method. RESULTS: 10 (10.9%) of 92 isolates were resistant to colistin and 80 (86.9%) to sulbactam. With the automation system, only 2 of 10 isolates were found resistant to colistin, while 8 isolates were susceptible. For this reason, the very major error rate of the Phoenix M50 automatic system among resistant isolates was determined as 8/10. It was determined that 6 (12%) of the colistin/sulbactam combination had a synergistic effect and 44 (88%) had an additive interaction. No antagonistic interaction was detected with the colistin-sulbactam combination in this study. CONCLUSION: A. baumannii strains should be confirmed by the broth microdilution method, which is the reference method, against the MIC results detected by automated systems. It was concluded that the use of colistin alone should be avoided in the treatment of A. baumannii infections.

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

An automatic identification system for citrus greening disease (Huanglongbing) using a YOLO convolutional neural network.

Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, "red-nose" fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the 'HLBdetector' app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly 'HLBdetector' app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

Comprehensive analyses of agrochemicals affecting aquatic ecosystems: A case study of Odonata communities and macrophytes in Saga Plain, northern Kyushu, Japan.

The negative influence of agrochemicals (pesticides: insecticide, fungicide, and herbicide) on biodiversity is a major ecological concern. In recent decades, many insect species are reported to have rapidly declined worldwide, and pesticides, including neonicotinoids and fipronil, are suspected to be partially responsible. In Japan, application of systemic insecticides to nursery boxes in rice paddies is considered to have caused rapid declines in Sympetrum (Odonata: Libellulidae) and other dragonfly and damselfly populations since the 1990s. In addition to the direct lethal effects of pesticides, agrochemicals indirectly affect Odonata populations through reductions in macrophytes, which provide a habitat, and prey organisms. Due to technical restrictions, most previous studies first selected target chemicals and then analyzed their influence on focal organisms at various levels, from the laboratory to the field. However, in natural and agricultural environments, various chemicals co-occur and can act synergistically. Under such circumstances, targeted analyses might lead to spurious correlations between a target chemical and the abundance of organisms. To address such problems, in this study we adopted a novel technique, "Comprehensive Target Analysis with an Automated Identification and Quantification System (CTA-AIQS)" to detect wide range of agrochemicals in water environment. The relationships between a wide range of pesticides and lentic Odonata communities were surveyed in agricultural and non-agricultural areas in Saga Plain, Kyushu, Japan. We detected significant negative relationships between several insecticides, i.e., acephate, clothianidin, dinotefuran, flubendiamide, pymetrozine, and thiametoxam (marginal for benthic odonates) and the abundance of lentic Epiprocta and benthic Odonates. In contrast, the herbicides we detected were not significantly related to the abundance of aquatic macrophytes, suggesting a lower impact of herbicides on aquatic vegetation at the field level. These results highlight the need for further assessments of the influence of non-neonicotinoid insecticides on aquatic organisms.

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries.

Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide.

Evaluation of identification accuracy using AIQS for GC-MS for measuring heavily contaminated samples.

Automated identification and quantification systems with gas chromatography-mass spectrometry (GC-MS) (i.e., AIQS-GC) are used as a simple and comprehensive method for screening chemicals existing in the environment and are expected to be useful for emergency surveys in the event of a disaster. However, reports on the potential of AIQS-GC in heavily contaminated samples (HCSs) are limited. In this study, the identification performance of AIQS-GC was confirmed by comparing the exact mass of the targets identified by AIQS-GC with the measured accurate mass using GC-quadrupole-time-of-flight MS (GC-QTofMS) and by employing firefighting wastewater as HCS. In HCS, the mass spectrum interference was determined to cause false positives. The GC-QTofMS method revealed the presence of false positives and the false rate of AIQS-GC in HCS. Herein, AIQS-GC showed high identification accuracy in a normal sample such as river water. Conversely, in HCS, AIQS-GC may lead to incorrect evaluations. The combination of AIQS-GC and support method using GC-QTofMS, which can avoid the false positive is extremely useful for the rapid and easy analysis of HCS.

Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images.

AIM: To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic. SETTINGS AND SAMPLE POPULATION: A total of 2385 digital lateral cephalograms (University data [1785]; Clinic F [300]; Clinic N [300]) were used. Using data from the university and clinics F and N, and combined data from clinics F and N, 50 cephalograms were randomly selected to test the system's performance (Test-data O, F, N, FN). MATERIALS AND METHODS: To examine the recognition ability of landmark positions of the AI system developed in Part I (Original System) for other clinical data, test data F, N and FN were applied to the original system, and success rates were calculated. Then, to determine the approximate number of cephalograms needed to re-learn for different quality images, 85 and 170 cephalograms were randomly selected from each group and used for the re-learning (F85, F170, N85, N170, FN85 and FN170) of the original system. To estimate the number of cephalograms needed for re-learning, we examined the changes in the success rate of the re-trained systems and compared them with the original system. Re-trained systems F85 and F170 were evaluated with test data F, N85 and N170 from test data N, and FN85 and FN170 from test data FN. RESULTS: For systems using F, N and FN, it was determined that 85, 170 and 85 cephalograms, respectively, were required for re-learning. CONCLUSIONS: The number of cephalograms needed to re-learn for images of different quality was estimated.

Clinical applicability of automated cephalometric landmark identification: Part I-Patient-related identification errors.

OBJECTIVES: To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors. SETTING AND SAMPLE POPULATION: The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet. MATERIALS AND METHODS: An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model. RESULTS: The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05). CONCLUSION: Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.

A deep learning approach to identify blepharoptosis by convolutional neural networks.

PURPOSE: Blepharoptosis is a known cause of reversible vision loss. Accurate assessment can be difficult, especially amongst non-specialists. Existing automated techniques disrupt clinical workflow by requiring user input, or placement of reference markers. Neural networks are known to be effective in image classification tasks. We aim to develop an algorithm that can accurately identify blepharoptosis from a clinical photo. METHODS: A total of 500 clinical photographs from patients with and without blepharoptosis were sourced from a tertiary ophthalmic center in Taiwan. Images were labeled by two oculoplastic surgeons, with an independent third oculoplastic surgeon to adjudicate disagreements. These images were used to train a series of convolutional neural networks (CNNs) to ascertain the best CNN architecture for this particular task. RESULTS: Of the models that trained on the dataset, most were able to identify ptosis images with reasonable accuracy. We found the best performing model to use the DenseNet121 architecture without pre-training which achieved a sensitivity of 90.1 % with a specificity of 82.4 %, compared to the worst performing model which was used a Resnet34 architecture with pre-training, achieving a sensitivity of 74.1 %, and specificity of 63.6 %. Models with and without pre-training performed similarly (mean accuracy 82.6 % vs. 85.8 % respectively, p = 0.06), though models with pre-training took less time to train (1-minute vs. 16 min, p < 0.01). CONCLUSIONS: We report the use of AI to accurately diagnose blepharoptosis from a clinical photograph with no external reference markers or user input requirement. Most current-generation CNN architectures performed reasonably on this task, with the DenseNet121, and Resnet18 architectures without pre-training performing best in our dataset.

Use of comprehensive target analysis for determination of contaminants of emerging concern in a sediment core collected from Beppu Bay, Japan.

In recent years, concern about the release of anthropogenic organic micropollutants referred to as contaminants of emerging concern (CECs) has been growing. The objective of this study was to find potential CECs by means of an analytical screening method referred to as comprehensive target analysis with an automated identification and quantification system (CTA-AIQS), which uses gas and liquid chromatography combined with mass spectrometry (GC-MS and LC-QTOF-MS). We used CTA-AIQS to analyze samples from a sediment core collected in Beppu Bay, Japan. With this method, we detected 80 compounds in the samples and CTA-AIQA could work to useful tool to find CECs in environmental media. Among the detected chemicals, three PAHs (anthracene, chrysene, and fluoranthene) and tris(isopropylphenyl)phosphate (TIPPP) isomers were found to increase in concentration with decreasing sediment depth. We quantified TIPPP isomers in the samples by means of targeted analysis using LC-MS/MS for confirmation. The concentration profiles, combined with previous reports indicating persistent, bioaccumulative, and toxic properties, suggest that these chemicals can be categorized as potential CECs in marine environments.

Web-based Fully Automated Cephalometric Analysis: Comparisons between App-aided, Computerized, and Manual Tracings.

OBJECTIVE: To compare the accuracy of cephalometric analyses made with fully automated tracings, computerized tracing, and app-aided tracings with equivalent hand-traced measurements, and to evaluate the tracing time for each cephalometric analysis method. METHODS: Pre-treatment lateral cephalometric radiographs of 40 patients were randomly selected. Eight angular and 4 linear parameters were measured by 1 operator using 3 methods: computerized tracing with software Dolphin Imaging 13.01(Dolphin Imaging and Management Solutions, Chatsworth, Calif, USA), app-aided tracing using the CephNinja 3.51 app (Cyncronus LLC, WA, USA), and web-based fully automated tracing with CephX (ORCA Dental AI, Las Vegas, NV). Correction of CephX landmarks was also made. Manual tracings were performed by 3 operators. Remeasurement of 15 radiographs was carried out to determine the intra-examiner and inter-examiner (manual tracings) correlation coefficient (ICC). Inter-group comparisons were made with one-way analysis of variance. The Tukey test was used for post hoc testing. RESULTS: Overall, greater variability was found with CephX compared with the other methods. Differences in GoGn-SN (°), I-NA (°), I-NB (°), I-NA (mm), and I-NB (mm) were statistically (p<0.05) and clinically significant using CephX, whereas CephNinja and Dolphin were comparable to manual tracings. Correction of CephX landmarks gave similar results to CephNinja and Dolphin. All the ICCs exceeded 0.85, except for I-NA (°), I-NB (°), and I-NB (mm), which were traced with CephX. The shortest analyzing time was obtained with CephX. CONCLUSION: Fully automatic analysis with CephX needs to be more reliable. However, CephX analysis with manual correction is promising for use in clinical practice because it is comparable to CephNinja and Dolphin, and the analyzing time is significantly shorter.

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania.

BACKGROUND: Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. RESULTS: Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sex-mediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shape-based sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. CONCLUSION: Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses.