Test Accuracy Research Articles

Antioxidant Assays in Phytonutrient Research: Translating Laboratory Innovations into Practical Applications

Background: There is great promise for improving nutrition and health thanks to phytonutrients' antioxidant qualities and health advantages. Their capacity to combat oxidative stress and associated illnesses emphasizes the importance of precisely evaluating their antioxidant characteristics. Objective: This study concludes by providing a comprehensive and critical critique of the current approaches to measure the antioxidant activity of phytonutrients. It dives into the fundamentals, benefits, drawbacks, and most recent developments of commonly used antioxidant assays, giving the reader a comprehensive grasp of the topic. This recapitulation of the review's goal in the end reinforces the reader's primary takeaway. Methods: Research on several antioxidant tests, such as FRAP, ORAC, DPPH, and ABTS, is consolidated in this review. It looks at each assay's performance traits, technological advances, and techniques. The review also assesses the incorporation of many assays to thoroughly examine phytonutrient potency and its uses in the food industry and nutritional science. Results: The review shows how antioxidant tests have advanced significantly, improving sensitivity, accuracy, and physiological relevance. It demonstrates how these tests can be used practically to guarantee food quality, create supplements, and offer nutritional advice. The paper also lists the difficulties today, including the intricacy of antioxidant mechanisms, test variability, and the requirement for assay standardization. The practical value of the research is emphasized by highlighting the significance of antioxidant tests for quality assurance, adulteration detection, and shelf life extension in the food business. Discussion: Scientists, doctors, and business experts interested in evaluating and applying phytonutrients will find this review helpful. It emphasizes how crucial it is to improve antioxidant testing to ascertain the possible health advantages and therapeutic uses of phytonutrients. The review highlights the need for increased test sensitivity, accuracy, and relevance while discussing the benefits and drawbacks of the available techniques. It draws attention to the importance of strong and trustworthy antioxidant tests to maximize the use of phytonutrients in food quality control and pharmaceutical research. Prospects: Future directions seek to address the challenges discovered through the development of multidisciplinary research and testing technologies. Novel approaches will advance our knowledge of phytonutrient potency and aid in developing medicines and nutraceuticals. This study highlights the significance of trustworthy assays for understanding and utilizing phytonutrients, providing academics and professionals in the business with vital insights.

In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.

The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169. The results of MGD classifications by three ophthalmologists were used to calculate the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, true negative rate (TNR), true positive rate (TPR), and false positive rate (FPR) of the model. The deep learning (DL) was used to build the model to identify the IVCM images. Model accuracy and loss tests showed that the DCNN model had high accuracy, low loss, and no large fluctuations at an epoch of 175, indicating that DenseNet-169 could enable the dichotomization to proceed stably. The accuracy of each classification of the test set was above 90%, which was highly consistent with the ophthalmologists' diagnosis. The precision of the groups in each classification was more than 90%, or even close to 100%, except for the meibomian gland atrophy with obstruction group in the fifth classification. The recall ranged from 0.8728 to 0.9981, and the FPR was low in the screening and classification diagnoses. The application of DCNN can achieve accurate classification and diagnosis of MGD through IVCM images and has great potential during medical procedures.

A Novel Hybrid Methodology Based on Transfer Learning, Machine Learning, and ReliefF for Chickpea Seed Variety Classification

Seed quality is a critical factor in crop production. Therefore, seed classification is required to obtain high-quality seeds and to enhance agricultural sustainability and productivity. This study focuses on the varietal classification of chickpeas, an important source of protein and fiber. Chickpea seed varieties can currently be identified by domain experts; their reliability and efficiency depend on the experience and skills of experts and are prone to human error. The design of classification models with high accuracy to assist in selection mechanisms is required for chickpea varieties. In this study, a novel hybrid methodology is proposed for the chickpea classification problem. This methodology combines three well-suited and robust components: feature extraction using three pre-trained models, feature selection with the ReliefF algorithm, and classification employing classical machine learning methods to enhance classification accuracy and efficiency. Various experiments have been conducted using the four hybrid models developed. Their performance has been compared in terms of accuracy, recall, F1-score, precision, and AUC. TL+SVM and TL+LDA outperformed the other models, with test accuracies of 94.4% and 94%, respectively. These results demonstrate the potential of a powerful model that will be beneficial as a component of computer vision systems in smart agriculture applications.

Imaging tests as predictors of progression to rheumatoid arthritis in clinically suspect arthralgia: A systematic review & meta-analysis.

To determine and compare the diagnostic accuracy of imaging tests for the prediction of RA progression in people with inflammatory joint pain or CSA. We searched MEDLINE, Embase and Web of Science from 1987 to March 2024. Studies evaluating any imaging tests in participants with inflammatory joint pain or CSA, without clinical synovitis were eligible. Reference standards included RA classification criteria, methotrexate initiation or development of inflammatory arthritis (IA). Two authors independently extracted data and assessed validity according to QUADAS-2. We estimated summary sensitivities and specificities for each imaging characteristic and fitted bivariate and hierarchical SROC models for meta-analysis where possible. We found 39 eligible studies including 42 cohorts, of which 12 evaluated MRI (n = 2,782; 19% with RA/IA), 26 evaluated ultrasound (US)(n = 6,805; 25% with RA/IA) and 10 evaluated other imaging tests (n = 3,362; 20% with RA/IA). Summary sensitivity and specificity for US Power Doppler ≥1 in at least one joint were 37% (95%CI 18%-60%) and 90% (95%CI 82%-94%), respectively (7 studies). Summary sensitivity and specificity for MRI synovitis in at least one joint were 45% (95%CI 29%-62%) and 84% (95%CI 66%-94%), respectively (4 studies). Lack of consensus regarding positive threshold definitions limited meta-analysis for other imaging features. Evidence for MRI or US in predicting RA/IA in people with CSA is heterogeneous and of variable quality. Further studies with larger sample sizes, longer follow-up times and uniform imaging test scoring, are warranted to determine whether imaging characteristics, in combination with clinical information, can predict RA in this population. PROSPERO: https://www.crd.york.ac.uk/prospero CRD42024501243.

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt. The CVC ClinicDB dataset supports this process, containing 1650 colonoscopy images classified as polyps or non-polyps. The best performance in the ensembles was done by the AD-22 + Transformer + SVM model, with an AUC of 0.99, a training accuracy of 99.50%, and a testing accuracy of 99.00%. This group also saw a high accuracy of 97.50% for Polyps and 99.30% for Non-Polyps, together with a recall of 97.80% for Polyps and 98.90% for Non-Polyps, hence performing very well in identifying both cancerous and healthy regions. The framework proposed here uses K-means clustering in combination with the visualisation of bounding boxes, thereby improving segmentation and yielding a silhouette score of 0.73 with the best cluster configuration. It discusses how to combine feature interpretation challenges into medical imaging for accurate localization and precise segmentation of malignant regions. A good balance between performance and generalization shall be done by hyperparameter optimization-heavy learning rates; dropout rates and overfitting shall be suppressed effectively. The hybrid schema of this work treats the deficiencies of the previous approaches, such as incorporating CNN-based effective feature extraction, Transformer networks for developing attention mechanisms, and finally the fine decision boundary of the support vector machine. Further, we refine this process via unsupervised clustering for the purpose of enhancing the visualisation of such a procedure. Such a holistic framework, hence, further boosts classification and segmentation results by generating understandable outcomes for more rigorous benchmarking of detecting colorectal cancer and with higher reality towards clinical application feasibility.

Using Pathogenic Escherichia coli Type III Secreted Effectors espK and espV as Markers to Reduce the Risk of Potentially Enterohemorrhagic Shiga Toxin-Producing Escherichia coli in Beef

Contamination of beef by certain strains of Shiga toxin-producing Escherichia coli (STEC) called enterohemorrhagic E. coli (EHEC) can lead to outbreaks of severe disease. Therefore, accurate monitoring tests are needed to identify high risk beef products and divert them from consumers. Most EHEC testing focuses on the detection of their key virulence factors Shiga toxin (stx) and intimin (eae). However, these two factors can occur separately in lower risk nonpathogenic E. coli (STEC and enteropathogenic E. coli; EPEC) and confound testing if both are present. Accessory virulence factors like the Type III secreted effectors espK and espV may aid in increasing the specificity of EHEC testing. This work first evaluated collections of EHEC (n = 83), STEC (n = 100) and EPEC (n = 95), finding espK and/or espV in 100%, 0%, and 60% of each, respectively. Next, an inoculation study of beef trim samples (n = 118) examined the ability of including espK and espV in the monitoring test scheme to distinguish samples inoculated with EHEC from those inoculated with mixtures of STEC and EPEC (non-EHEC). Test accuracy was calculated as Area Under the Receiver Operating Characteristic curve (AUC) and found to be significantly (p < 0.05) different, increasing from 68.0% (stx/eae) to 76.8% by including espK and espV. Finally, 361 regulatory agency beef samples that had been identified as suspect for EHEC (stx+/eae+) were examined with the addition of espK and espV, and results compared to culture isolation. Culture isolation identified 42 EHEC, 82 STEC, and 67 EPEC isolates in 146 of the samples. In the case of these naturally contaminated samples, inclusion of espK and espV increased test accuracy compared to culture isolation from an AUC of 50.5% (random agreement) to 69.8% (good agreement). Results show that the inclusion of espK and espV can increase the specificity of identifying high risk EHEC contaminated beef and release beef contaminated with nonpathogenic or low risk E. coli. Further, use of espK and espV identified samples contaminated by common EHEC of serogroups O157, O26, and O103, as well as of less common serogroups O182, O177, and O5.

Optimising deep learning models for ophthalmological disorder classification

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structural alterations in the optic disc, blood vessels, fovea, and macula. Patients frequently deal with various ophthalmological conditions in either one or both eyes. In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient’s left and right eyes. We compared the performance of two different optimisers, Stochastic Gradient Descent (SGD) and Adam, separately. The best result was achieved using the MobileNet model with the Adam optimiser, yielding a testing accuracy of 89.64%.

A comparative analysis and prediction of carbon emission in India using Machine learning models

<p style="line-height:150%;text-align:justify;text-indent:36.0pt;"><span style="background-color:yellow;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">Among the most significant issues that will impact and pose a major threat to our future are global warming and climate change. This study focuses on forecasting India's carbon emissions, specifically examining the role of agriculture and agri-related industries, using time-series data from 1990 to 2020. </span><span style="background-color:lime;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">To achieve this, a machine learning-based approach was proposed, utilizing five advanced models AdaBoost, XGB, Gradient Boosting, Random Forest, and Linear Regression. These models were selected for their ability to process large datasets, identify complex patterns, and provide precise predictions, offering a comprehensive framework for estimating carbon emissions. The approach aims to identify the most accurate predictive model for estimating carbon emissions, facilitating informed policy interventions and strategic planning. </span><span style="background-color:aqua;font-family:"Times New Roman","serif";font-size:12.0pt;line-height:150%;">The Carbon Budget 2023 research highlights that India's carbon emissions increased by 8.2% in 2023, raising significant concerns for environmental sustainability. Using the proposed machine learning models, the study facilitates robust data prediction. Among the five models, XGB demonstrates superior performance with evaluation metrics such as a Mean Absolute Error (MAE) of 19843, Root Mean Square Error (RMSE) of 24022, Mean Squared Error (MSE) of 5770998, R² value of 0.9, and test accuracy of 99%. These findings establish XGB as the most reliable model for forecasting carbon emissions. Consequently, XGB emerges as a powerful tool for accurately estimating emissions and addressing the challenges posed by climate change.</span></p>

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

Urinary Bladder Acute Inflammations and Nephritis of the Renal Pelvis: Diagnosis Using Fine-Tuned Large Language Models

Background: Large language models (LLMs) have seen a significant boost recently in the field of natural language processing (NLP) due to their capabilities in analyzing words. These autoregressive models prove robust in classification tasks where texts need to be analyzed and classified. Objectives: In this paper, we explore the power of base LLMs such as Generative Pre-trained Transformer 2 (GPT-2), Bidirectional Encoder Representations from Transformers (BERT), Distill-BERT, and TinyBERT in diagnosing acute inflammations of the urinary bladder and nephritis of the renal pelvis. Materials and Methods: the LLMs were trained and tested using supervised fine-tuning (SFT) on a dataset of 120 examples that include symptoms that may indicate the occurrence of these two conditions. Results: By employing a supervised fine-tuning method and carefully crafted prompts to present the data, we demonstrate the feasibility of using minimal training data to achieve a reasonable diagnostic, with overall testing accuracies of 100%, 100%, 94%, and 79%, for GPT-2, BERT, Distill-BERT, and TinyBERT, respectively.

Effectiveness of Domestic Wastewater Treatment Plant (WWTP) at Clinic Laboratory X (Case Study of Clinic Laboratory X in Pontianak City

This clinical laboratory plays a vital role in supporting the health sector by conducting tests and analyses related to diseases, health conditions, and factors affecting the community. However, these activities also generate domestic liquid waste that can pose a serious threat to the environment and public health if not managed effectively. Effective management of domestic liquid waste is crucial, given the importance of the clinical laboratory's role in supporting the health sector and the need for accurate testing.

A handheld milk conductivity sensing device (Mylee) for measuring secretory activation progress in lactating women: a device validation study

BackgroundHuman milk electrolytes are known biomarkers of stages of lactation in the first weeks after birth. However, methods for measuring milk electrolytes are available only in laboratory or expert settings. A small handheld milk sensing device (Mylee) capable of determining on-site individual secretory activation progress from sensing the conductivity of a tiny milk specimen was developed. Here we evaluate the validity of a novel milk-sensing device (Mylee) for measuring the progress of milk maturation and secretory activation status.MethodsRetrospective data analysis of laboratory records generated using the Mylee device. Device conductivity measurements were assessed for accuracy, reliability and stability in rigorous laboratory tests with standard materials. A set of human milk specimens (n = 167) was used to analyze the agreement between the milk maturation score and laboratory measurements of the secretory activation biomarker milk sodium [Na+].ResultsThe Mylee device was demonstrated to have excellent reproducibility (CV95%<5%) and accuracy (error < 5%) for conductivity measurements of a small specimen (350 µl), with good device stability and almost perfect inter-device unit reliability (ICC > 0.90). With regression analysis, we revealed excellent agreement between Mylee milk maturation (MM%) output or its raw conductivity signal and laboratory measurements of conductivity and sodium [Na+] in a dataset of milk specimens (n = 167; R2 > 0.9). The Mylee MM% score showed good predictive ability for secretary activation status, as determined by sodium threshold (18 mmol/L) in human milk specimens.ConclusionsIn this study, we demonstrated the reliability and validity of the Mylee device and its ability to detect on-site milk secretory activation in a manner comparable to that of electrolyte-based methods. The novel MyLee device offers the potential to generate real-time information about the lactation stage, measured by mothers at the commodity of their home.

Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans

Purpose This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics. Methods The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups. The best neural network model was optimized for better outcomes. Prediction accuracy, the area under the curve, sensitivity, specificity, precision, and F-1 score values were obtained. Results The Wilcoxon Signed-Rank test provided significantly higher validation accuracy for neural networks than decision trees, discriminant analysis, support vector machines, and k-nearest neighbor classifiers (p = 0.005 for all). For C57BL/6-DBA/2J classification, a mean validation accuracy of 88.11 ± 3.92% (95% CI: 86.99-89.22) was achieved for the neural network when the optimized neural network had 92.31% final test accuracy with an area under the curve value of 0.9762, 94.44% sensitivity, 90.48% specificity, 89.47% precision, and 0.92 F-1 score. The optimized neural network model for age group differentiation had a final test accuracy of 82.05% with a 0.9064 area under the curve value, 77.27% sensitivity, 88.24% specificity, 89.47% precision, and 0.83 F-1 score. Conclusions These findings validate that machine learning, using segmentation metrics instead of images, can effectively analyze retinal OCT scans in mice for categorical predictions in experimental models. Expanding this approach with additional features, including histopathological and functional correlations, is expected to improve the prediction power further, promising valuable applications to predict more complex outcomes in experimental and clinical studies.

Diagnostic Accuracy of Rapid Antigen Tests for Trichomoniasis: A Meta-analysis.

Trichomoniasis is a globally prevalent sexually transmitted infection caused by the protozoan Trichomonas vaginalis. Polymerase chain reaction (PCR) is the gold standard for diagnosing trichomoniasis, but it is expensive. Antigen tests are immunochromatographic immunoassays that detect T. vaginalis membrane proteins. Despite being user-friendly and rapid, the diagnostic accuracy of antigen tests remains uncertain. Therefore, this meta-analysis aimed to evaluate the diagnostic accuracy of antigen tests for T. vaginalis infections. We mined the PubMed, Embase, and Cochrane Library databases for studies evaluating the diagnostic accuracy of antigen tests for T. vaginalis. We included studies that provided diagnostic test accuracy data in order to conduct a meta-analysis. We evaluated antigen tests based on immunochromatography and lateral flow devices. The meta-analysis was conducted by using the hierarchical summary receiver operating characteristic model. Eleven studies with 5,884 samples were included. The meta-analysis yielded a pooled sensitivity of 87.0% and a pooled specificity of 98.3%. A subgroup analysis employing PCR as the reference standard yielded a sensitivity of 58.5%, whereas another subgroup analysis using culture returned a sensitivity of 95.9%. The subgroup analysis of 6 studies comprising 2,328 specimens from symptomatic individuals yielded a pooled sensitivity of 85% and a specificity of 99.9%. The antigen tests exhibited high sensitivity and specificity. Additionally, subgroup analyses revealed that antigen tests demonstrated greater sensitivity in diagnosing symptomatic patients compared to asymptomatic individuals. While less sensitive than PCR, antigen testing remains a promising avenue for detecting T. vaginalis infections.

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI — functional connectivity, gradients, or gradient dispersion — best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann–Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.

A comparative analysis of CNNs and LSTMs for ECG-based diagnosis of arrythmia and congestive heart failure

Cardiac arrhythmias are major global health concern and their early detection is critical for diagnosis. This study comprehensively evaluates the effectiveness of CNNs and LSTMs for the classification of cardiac arrhythmias, considering three PhysioNet datasets. ECG records are segmented to accommodate around ∼10s of ECG data. Followed by transformation to scalograms using DWT for training VGG-16; and WTS for feature extraction and dimensionality reduction for training LSTM network. VGG-16 achieved 96.44% test accuracy while LSTM achieved 92%. Results also highlight the effectiveness of VGG-16 for short-duration ECG analysis, while LSTM excels in long-term monitoring on edge devices for personalized healthcare.

Arecanut Disease Detection

The tropical crop arecanut, sometimes referred to as betel nut, is primarily farmed in India. In terms of arecanut production and consumption, the nation ranks second in the world. The areca nut plant is vulnerable to numerous diseases that impact its roots, stem, leaves, and fruits throughout its life cycle. While some of these illnesses can be seen with the naked eye, others cannot. These illnesses are brought on by abrupt changes in temperature and other meteorological factors; early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and precise disease diagnosis. Using convolutional neural networks, we developed a system that assists in identifying arecanut, leaf, and trunk ailments and offers treatments. A Convolutional Neural Network (CNN) is a Deep Learning method that uses an image as input, gives different items in the image learnable weights and biases, and then uses the results to determine which objects are different. We took a dataset with 620 photos of arecanuts in both good and unhealthy conditions in order to train and evaluate the CNN model. An 80:20 ratio is used to separate the test and train data. Adam serves as the optimizer function, accuracy serves as a measure, and categorical cross-entropy serves as the loss function for the model compilation. To attain high validation and test accuracy with little loss, the model is trained over a total of 5 epochs. It was discovered that the suggested method was successful and 88.46 percent accurate in detecting arecanut illness. Diseases frequently seen in areca trees include Mahali Disease (Koleroga), Bud Rot Disorder, Stem Exudation, Yellow Leaf Blotch,Yellow Disease, which arises from persistent rainfall and climate alterations, these ailments need to be managed in the initial phase of infection; otherwise, it could lead to difficulties in oversight in the concluding phase that could result in detriment to the latter. To prevent this, we can utilize Machine Learning for disease detection. We will identify yellow leaf spot, stem bleeding, and Mahali disease (KoleRoga) in this project and provide treatments for the conditions we find. Key words: Convolution Neural Networks, Arecanut, and Machine Learning

Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients

Background and purpose. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided breast cancer patients during the initial computed tomography (CT) appointment.Materials and methods. Anatomic distances were extracted from a single-institution dataset of free breathing (FB) CT scans from locoregional right-sided breast cancer patients. Models were developed using combinations of anatomic distances and ML classification algorithms (gradient boosting, k-nearest neighbors, logistic regression, random forest, and support vector machine) and optimized over 100 iterations using stratified 5-fold cross-validation. Models were grouped by the number of anatomic distances used during development; those with the highest validation accuracy were selected as final models. Final models were compared based on their predictive ability, measurement collection efficiency, and robustness to simulated user error during measurement collection.Results. This retrospective study included 238 patients treated between 2016 and 2021. Model development ended once eight anatomic distances were included, and the validation accuracy plateaued. The best performing model used logistic regression with four anatomic distances achieving 80.5% average testing accuracy, with minimal false negatives and positives (<27%). The anatomic distances required for prediction were collected within 3 min and were robust to simulated user error during measurement collection, changing accuracy by <5%.Conclusion. Our logistic regression model using four anatomic distances provided the best balance between efficiency, robustness, and ability to predict if DIBH was needed for locoregional right-sided breast cancer patients.

P0430 Central reading of ulcerative colitis endoscopic disease activity: Automated but with a human in the loop (2M+1H)

Abstract Background Central reading in ulcerative colitis (UC) trials has high interobserver variability, typically managed through 2 + 1 adjudication when 2 readers disagree on the endoscopic subscore (40% of cases)1,2. While artificial intelligence (AI) models show high agreement with human readers3, relying solely on AI in medical imaging raises concerns. Our novel 2M+1H approach uses human adjudication (H) only when two independently developed AI models (2M) disagree, preserving human oversight while affording the reproducibility and efficiency provided by AI4. The models’ independent development and performance characteristics ensure distinct “personalities,” similar to independent human readers. Methods Both models 1 and 2 were previously developed and validated deep learning models. In addition to distinct approaches, training was conducted by separate teams utilizing different training data. Model 1 was trained using a self-supervised feature extractor while model 2 was trained on supervised severity models consisting only of human-identifiable features. A panel of 10 blinded central readers (CRs) were selected and randomly assigned to provide independent human reads. 150 lower endoscopy videos from routine practice with a confirmed diagnosis of UC were independently assessed through the traditional 2 + 1 approach and the 2M+1H approach. The “final score” generated by each reading workflow was compared using quadratic weighted kappa (QWK). Noninferiority was determined if the lower bounds of the 95% confidence intervals (CIs), computed via bootstrapping, exceeded the QWK margin-adjusted threshold (set at -10%). Results The 2M+1H approach achieved good agreement with traditional 2 + 1 human central reading (QWK 0.78, 95% CI 0.69-0.84) and is statistically noninferior (Fig 1). Binary classification of endoscopic improvement and endoscopic remission was very good (agreement rate 82.7% (95% CI 76.7%-88.7%) and 89.3% (95% CI 84.7%-94.0%). Comparison of the two ML models showed statistically similar behavior to that of the first two human CRs in the 2 + 1 approach (QWK model 1 vs model 2: 0.74 (95% CI 0.66-0.80), QWK CR 1 vs CR2: 0.78 (95% CI 0.70-0.85), p = 0.80). The 2M+1H approach reduced the total number of human reads from 349 (2 + 1) to 67 (2M+1H), an 81% decrease, decreasing the total number of human reads per video (0.45 vs 2.33, p &amp;lt; 0.001) (Fig 2). Conclusion Our data demonstrates that 2M+1H central reading achieved comparable performance to traditional 2 + 1 human central reading and is statistically noninferior while significantly reducing human workload. Prospective studies may adopt this approach to leverage ML as a regulatory-aligned alternative to the 2 + 1 human reading approach in UC clinical trials. References Food and Drug Administration. Ulcerative Colitis: Developing Drugs for Treatment [Internet]. 2022 [cited 2024 Oct 17]. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/ulcerative-colitis-developing-drugs-treatment Gottlieb, K., Daperno, M., Usiskin, K., Sands, B. E., Ahmad, H., Howden, C. W., . . . &amp; Reinisch, W. (2021). Endoscopy and central reading in inflammatory bowel disease clinical trials: Achievements, challenges and future developments. Gut, 70(2), 418-426. Rimondi, A., Gottlieb, K., Despott, E. J., Iacucci, M., Murino, A., &amp; Tontini, G. E. (2023). Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Digestive and Liver Disease, 56(7):1164-1172. Cohen, I. G., Babic, B., Gerke, S., Xia, Q., Evgeniou, T., &amp; Wertenbroch, K. (2023). How AI can learn from the law: Putting humans in the loop only on appeal. npj Digital Medicine, 6(1), 160.

A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm

The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer’s active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model’s prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.