Optimization-enabled deep learning model for disease detection in IoT platform

Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson's correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).

Clinicopathological and molecular predictors of [18F]FDG-PET disease detection in HER2-positive early breast cancer: RESPONSE, a substudy of the randomized PHERGain trial

BACKGROUND

The PHERGain study (NCT03161353) is assessing early metabolic responses to neoadjuvant treatment with trastuzumab-pertuzumab and chemotherapy de-escalation using a [18Fluorine]fluorodeoxyglucose-positron emission tomography ([18F]FDG-PET) and a pathological complete response-adapted strategy in HER2-positive (HER2+) early breast cancer (EBC). Herein, we present RESPONSE, a PHERGain substudy, where clinicopathological and molecular predictors of [18F]FDG-PET disease detection were evaluated.

METHODS

A total of 500 patients with HER2 + EBC screened in the PHERGain trial with a tumor size > 1.5 cm by magnetic resonance imaging (MRI) were included in the RESPONSE substudy. PET[-] criteria entailed the absence of  ≥ 1 breast lesion with maximum standardized uptake value (SUVmax) ≥ 1.5 × SUVmean liver + 2 standard deviation. Among 75 PET[-] patients screened, 21 with SUVmax levels < 2.5 were randomly selected and matched with 21 PET[+] patients with SUVmax levels ≥ 2.5 based on patient characteristics associated with [18F]FDG-PET status. The association between baseline SUVmax and [18F]FDG-PET status ([-] or [+]) with clinicopathological characteristics was assessed. In addition, evaluation of stromal tumor-infiltrating lymphocytes (sTILs) and gene expression analysis using PAM50 and Vantage 3D™ Cancer Metabolism Panel were specifically compared in a matched cohort of excluded and enrolled patients based on the [18F]FDG-PET eligibility criteria.

RESULTS

Median SUVmax at baseline was 7.2 (range, 1-39.3). Among all analyzed patients, a higher SUVmax was associated with a higher tumor stage, larger tumor size, lymph node involvement, hormone receptor-negative status, higher HER2 protein expression, increased Ki67 proliferation index, and higher histological grade (p < 0.05). [18F]FDG-PET [-] criteria patients had smaller tumor size (p = 0.014) along with the absence of lymph node involvement and lower histological grade than [18F]FDG-PET [+] patients (p < 0.01). Although no difference in the levels of sTILs was found among 42 matched [18F]FDG-PET [-]/[+] criteria patients (p = 0.73), [18F]FDG-PET [-] criteria patients showed a decreased risk of recurrence (ROR) and a lower proportion of PAM50 HER2-enriched subtype than [18F]FDG-PET[+] patients (p < 0.05). Differences in the expression of genes involved in cancer metabolism were observed between [18F]FDG-PET [-] and [18F]FDG-PET[+] criteria patients.

CONCLUSIONS

These results highlight the clinical, biological, and metabolic heterogeneity of HER2+ breast cancer, which may facilitate the selection of HER2+ EBC patients likely to benefit from [18F]FDG-PET imaging as a tool to guide therapy.

TRIAL REGISTRATION

Clinicaltrials.gov; NCT03161353; registration date: May 15, 2017.

Empowering agricultural research: A comprehensive custard apple (Annona squamosa) disease dataset for precise detection

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants. Utilizing methods like deep learning, feature extraction, and pattern recognition, this dataset sharpens automated disease identification precision. Its extensive range suits testing and training disease identification techniques. Public access fosters collaboration, fast-tracking plant pathology advancements and supporting Custard Apple plant sustainability. This dataset fosters collaborative efforts, aiding disease prevention techniques to boost Custard Apple yield and refine farming. It enhances disease identification, monitoring, and management in Custard Apple production, aiming to elevate agricultural practices and crop yields.

Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT

Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer

Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Coconut (Cocos nucifera) tree disease dataset: A dataset for disease detection and classification for machine learning applications

The ``Coconut (Cocos nucifera) Tree Disease Dataset'' comprises 5,798 images across five disease categories: ``Bud Root Dropping,'' ``Bud Rot,'' ``Gray Leaf Spot,'' ``Leaf Rot,'' and ``Stem Bleeding.'' This dataset is intended for machine learning applications, facilitating disease detection and classification in coconut trees. The dataset's diversity and size make it suitable for training and evaluating disease detection models. The availability of this dataset will support advancements in plant pathology and aid in the sustainable management of coconut plantations. By providing a valuable resource for researchers, this dataset contributes to improved disease management and sustainable coconut plantation practices.

A comprehensive review on federated learning based models for healthcare applications

A disease is an abnormal condition that negatively impacts the functioning of the human body. Pathology determines the causes behind the disease and identifies its development mechanism and functional consequences. Each disease has different identification methods, including X-ray scans for pneumonia, covid-19, and lung cancer, whereas biopsy and CT-scan can identify the presence of skin cancer and Alzheimer's disease, respectively. Early disease detection leads to effective treatment and avoids abiding complications. Deep learning has provided a vast number of applications in medical sectors resulting in accurate and reliable early disease predictions. These models are utilized in the healthcare industry to provide supplementary assistance to doctors in identifying the presence of diseases. Majorly, these models are trained through secondary data sources since healthcare institutions refrain from sharing patients' private data to ensure confidentiality, which limits the effectiveness of deep learning models due to the requirement of extensive datasets for training to achieve optimal results. Federated learning deals with the data in such a way that it doesn't exploit the privacy of a patient's data. In this work, a wide variety of disease detection models trained through federated learning have been rigorously reviewed. This meta-analysis provides an in-depth review of the federated learning architectures, federated learning types, hyperparameters, dataset utilization details, aggregation techniques, performance measures, and augmentation methods applied in the existing models during the development phase. The review also highlights various open challenges associated with the disease detection models trained through federated learning for future research.

Animal use in detection of disease within pediatric populations

There is a need for novel techniques for disease detection in humans. Research has shown that using animals for detection of disease is a promising area of study. A literature review was conducted using the terms animals, disease detection, seizures, epilepsy, infectious disease, cancer, and pediatrics to determine the published literature to date of the use of animals in detection of disease. Research studies between 1999-2022 were included in this article. The published studies demonstrate that animals have been used for disease detection in seizures, infectious diseases, Type I diabetes mellitus, and cancer. However, these studies have predominantly focused on the adult patient population. There is limited data available regarding the use of animals in disease detection within pediatrics, which warrants further research into this topic.

A Voice Disease Detection Method Based on MFCCs and Shallow CNN

The incidence rate of voice diseases is increasing year by year. The use of software for remote diagnosis is a technical development trend and has important practical value. Among voice diseases, common diseases that cause hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and vocal cord polyp. This paper presents a voice disease detection method that can be applied in a wide range of clinical. We cooperated with Xiangya Hospital of Central South University to collect voice samples from 352 different patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are extracted as input features to describe the voice in the form of data. An innovative model combining MFCC parameters and single convolution layer CNN is proposed for fast calculation and classification. The highest accuracy we achieved was 92%, it is fully ahead of the original research results and internationally advanced. And we use advanced voice function assessment databases (AVFAD) to evaluate the generalization ability of the method we proposed, which achieved an accuracy rate of 98%. Experiments on clinical and standard datasets show that for the pathological detection of voice diseases, our method has greatly improved in accuracy and computational efficiency.

BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning

Bananas, one of the most widely consumed fruits globally, are highly susceptible to various leaf spot diseases, leading to significant economic losses in banana production. In this article, we present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. The dataset was used to develop the BananaSqueezeNet model [1]. The BananaLSD dataset contains 937 images of banana leaves collected from banana fields, which were then further augmented to generate another 1600 images. The images were acquired using three smartphone cameras in diverse real-world conditions. The dataset has potential for reuse in the development of machine learning models that can help farmers identify symptoms early. It can be useful for researchers working on leaf spot diseases and serve as motivation for further researches.

Visible - Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the monitoring of apple fire blight

This dataset consists of three groups of hyperspectral images of apple tree plants. The first group of images consists of a temporal monitoring of seven apple tree plants, infected with fire blight (Erwinia amylovora), and six control plants over a period of 15 days. The second group of images includes a temporal monitoring of three infected plants, seven plants subjected to water stress, and seven control plants. The third group of images corresponds to acquisitions made in the orchard on nine trees showing symptoms of fire blight and six control trees. The pixel locations of infected areas have been provided for all images featuring symptomatic plants.

IoT-based disease prediction using machine learning

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Automated monitoring and detection of disease using a generic facial feature scoring system - A case study on FMD infected cows

Digital images are becoming more readily available and possibilities for image processing are developing rapidly. This opens the possibility to use digital images to monitor and detect diseases in animals. In this paper we present 1) a generic facial feature scoring system based on seven facial features, 2) manual scores of images of Holstein Frisian heifers during foot-and-mouth disease vaccine efficacy trials and 3) automatic disease scores of the same animals. The automatic scoring system was based on the manual version and trained on annotated images from the manual scoring system. For both systems we found an increase in disease scores three days post infection, followed by a recovery. This temporal pattern matched with observations made by animal caretakers. Importantly, the automatic system was able to discern animals that were protected by the vaccine, and did not develop blisters at the feet, and animals that were not protected. Finally, automatic scores could be used to detect healthy and sick animals with a sensitivity and specificity of 0.94 on the second and third days following infection in an experimental setting. This generic facial feature disease scoring system could be further developed and extended to lactating Holstein Frisian dairy cows, other breeds and other infectious diseases. The system could be applied during animal experiments or, after further development, in a farm setting.

Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain

Remote sensing makes it possible to gather data rapidly, precisely, accurately, and non-destructively, allowing it to assess grapevines accurately in near real-time. In addition, multispectral cameras capture information in different bands, which can be combined to generate vegetation indices useful in precision agriculture. This dataset contains 16,504 multispectral images from a 1.06 ha vineyard affected by Botrytis cinerea, in the north of Spain. The photos were taken throughout four UAV flights at 30 m height with varying camera angles on 16 September 2021, the same date as the grape harvest. The first flight took place with the camera tilted at 0° (nadir angle), the second flight at 30°, the third flight at 45°, and the fourth flight was also performed at 0° but was scheduled in the afternoon to capture the shadows of the plants projected on the ground. This dataset was created to support researchers interested in disease detection and, in general, UAV remote sensing in vineyards and other woody crops. Moreover, it allows digital photogrammetry and 3D reconstruction in the context of precision agriculture, enabling the study of the effect of different tilt angles on the 3D reconstruction of the vineyard and the generation of orthomosaics.

LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis

Automatic detection of retinal diseases based on deep learning technology and Ultra-widefield (UWF) images plays an important role in clinical practices in recent years. However, due to small lesions and limited data samples, it is not easy to train a detection-accurate model with strong generalization ability. In this paper, we propose a lesion attention conditional generative adversarial network (LAC-GAN) to synthesize retinal images with realistic lesion details to improve the training of the disease detection model. Specifically, the generator takes the vessel mask and class label as the conditional inputs, and processes the random Gaussian noise by a series of residual block to generate the synthetic images. To focus on pathological information, we propose a lesion feature attention mechanism based on random forest (RF) method, which constructs its reverse activation network to activate the lesion features. For discriminator, a weight-sharing multi-discriminator is designed to improve the performance of model by affine transformations. Experimental results on multi-center UWF image datasets demonstrate that the proposed method can generate retinal images with reasonable details, which helps to enhance the performance of the disease detection model.

Pseudo high-frequency boosts the generalization of a convolutional neural network for cassava disease detection

Frequency is essential in signal transmission, especially in convolutional neural networks. It is vital to maintain the signal frequency in the neural network to maintain the performance of a convolutional neural network. Due to destructive signal transmission in convolutional neural network, signal frequency downconversion in channels results into incomplete spatial information. In communication theory, the number of Fourier series coefficients determines the integrity of the information transmitted in channels. Consequently, the number of Fourier series coefficients of the signals can be replenished to reduce the information transmission loss. To achieve this, the ArsenicNetPlus neural network was proposed for signal transmission modulation in detecting cassava diseases. First, multiattention was used to maintain the long-term dependency of the features of cassava diseases. Afterward, depthwise convolution was implemented to remove aliasing signals and downconvert before the sampling operation. Instance batch normalization algorithm was utilized to keep features in an appropriate form in the convolutional neural network channels. Finally, the ArsenicPlus block was implemented to generate pseudo high-frequency in the residual structure. The proposed method was tested on the Cassava Datasets and compared with the V2-ResNet-101, EfficientNet-B5, RepVGG-B3g4 and AlexNet. The results showed that the proposed method performed [Formula: see text] in terms of accuracy, 1.2440 in terms of loss, and [Formula: see text] in terms of the F1-score, outperforming the comparison algorithms.

A multi-label classification system for anomaly classification in electrocardiogram

Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.

Recent advances in the use of the CRISPR-Cas system for the detection of infectious pathogens

Infectious diseases cause great economic loss and individual and even social anguish. Existing detection methods lack sensitivity and specificity, have a poor turnaround time, and are dependent on expensive equipment. In recent years, the clustered regularly interspaced short palindromic repeats (CRISPR)‍-CRISPR-associated protein (Cas) system has been widely used in the detection of pathogens that cause infectious diseases owing to its high specificity, sensitivity, and speed, and good accessibility. In this review, we discuss the discovery and development of the CRISPR-Cas system, summarize related analysis and interpretation methods, and discuss the existing applications of CRISPR-based detection of infectious pathogens using Cas proteins. We conclude the challenges and prospects of the CRISPR-Cas system in the detection of pathogens.

Volatile biomarkers for non-invasive detection of American foulbrood, a threat to honey bee pollination services

Honey bees provide essential environmental services, pollinating both agricultural and natural ecosystems that are crucial for human health. However, these pollination services are under threat by outbreaks of the bacterial honey bee disease American foulbrood (AFB). Caused by the bacterium, Paenibacillus larvae, AFB kills honey bee larvae, converting the biomass to a foul smelling, spore-laden mass. Due to the bacterium's tough endospores, which are easily spread and extremely persistent, AFB management requires the destruction of infected colonies in many countries. AFB detection remains a significant problem for beekeepers: diagnosis is often slow, relying on beekeepers visually identifying symptoms in the colony and molecular confirmation. Delayed detection can result in large outbreaks during high-density beekeeping pollination events, jeopardising livelihoods and food security. In an effort to improve diagnostics, we investigated volatile compounds associated with AFB-diseased brood in vitro and in beehive air. Using Solid Phase Microextraction and Gas Chromatography Mass-Spectrometry, we identified 40 compounds as volatile biomarkers for AFB infections, including 16 compounds previously unreported in honey bee studies. In the field, we detected half of the biomarkers in situ (in beehive air) and demonstrated their sensitivity and accuracy for diagnosing AFB. The most sensitive volatile biomarker, 2,5-dimethylpyrazine, was exclusively detected in AFB-disease larvae and hives, and was detectable in beehives with <10 AFB-symptomatic larvae. These, to our knowledge, previously undescribed biomarkers are prime candidates to be targeted by a portable sensor device for rapid and non-invasive diagnosis of AFB in beehives.

Biomarker interaction selection and disease detection based on multivariate gain ratio

Background

Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It’s the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction.

Results

We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93.13\%$$\end{document}93.13% than I-score of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.73\%$$\end{document}91.73% in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.80\%$$\end{document}89.80% based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.32\%$$\end{document}97.32% compared to I-score with accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.11\%$$\end{document}89.11%. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset.

Conclusions

MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.

Machine Learning for Image Analysis: Leaf Disease Segmentation

Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.

Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes

Soft rot and Pythium leak are postharvest storage diseases of potato tubers that can cause substantial crop losses in the US. This study focused on detecting volatile organic compounds (VOCs) associated with rot inoculated tubers during storage (up to 21 days) using headspace solid-phase microextraction (SPME) coupled to gas chromatography (GC) with mass spectrometry (MS) and flame ionization detector (FID) analysis. Russet Burbank and Ranger Russet tubers were inoculated with the rot pathogens. Static sampling with 50 min trapping time followed by GC-MS and GC-FID analysis identified 23 and 30 common VOCs from the pathogen inoculated tubers. Overall, n,n-dimethylmethylamine, acetone, 1-undecene, and styrene, occurred frequently and repeatability in inoculated samples based on GC-MS analysis, with the latter two found using GC-FID analysis as well. Identification of such biomarkers can be useful in developing high-throughput VOC sensing systems for early disease detection in potato storage facilities.

Engineered nanomaterials in plant diseases: can we combat phytopathogens?

Engineered nanomaterials (ENM) have a high potential for use in several areas of agriculture including plant pathology. Nanoparticles (NPs) alone can be applied for disease management due to their antimicrobial properties. Moreover, nanobiosensors allow a rapid and sensitive diagnosis of pathogens because NPs can be conjugated with nucleic acids, proteins and other biomolecules. The use of ENM in diagnosis, delivery of fungicides and therapy is an eco-friendly and economically viable alternative. This review focuses on different promising studies concerning ENM used for plant disease management including viruses, fungi, oomycetes and bacteria; diagnosis and delivery of antimicrobials and factors affecting the efficacy of nanomaterials, entry, translocation and toxicity. Although much research is required on metallic NPs due to the possible risks to the final consumer, ENMs are undoubtedly very useful tools to achieve food security in the world. KEY POINTS: • Increasing global population and fungicides have necessitated alternative technologies. • Nanomaterials can be used for detection, delivery and therapy of plant diseases. • The toxicity issues and safety should be considered before the use of nanomaterials.

Detection of Posterior Segment Eye Disease in Rural Eye Camps in South India: A Nonrandomized Cluster Trial

PURPOSE

Rural screening camps in India historically have focused on detection of cataract and uncorrected refractive error. This study aimed to increase detection, referral, and follow-up for posterior segment diseases (PSDs) in rural eye camps using a novel technology-driven eye camp model.

DESIGN

A clustered nonrandomized trial in the catchment area of Aravind Eye Care System (AECS) Pondicherry, to compare 2 eye camp models: the traditional AECS eye camp model and the novel, technology-driven, eye camp model.

PARTICIPANTS

Patients 40 to 75 years of age who attended free camps conducted by AECS Pondicherry. Those with corneal pathologic features were excluded because this precluded an adequate view of the posterior segment to screen for PSD.

METHODS

The clinical protocols in the 2 arms were standardized and the same study team was used in both study arms. The unit of allocation to the 2 study arms was at the level of the eye camp, rather than the level of the individual study participant.

MAIN OUTCOME MEASURES

The primary study outcome was detection of suspected PSD (glaucoma, diabetic retinopathy, age-related macular degeneration, other PSDs). Secondary outcomes included: (1) the proportion of referred participants who underwent an examination at the base hospital and (2) the proportion with confirmed PSD on examination at the base hospital.

RESULTS

The study included 11 traditional and 18 novel eye camps with a total of 3048 participants (50% in each study arm). The mean age of all participants was 58.4 ± 9.1 years and 1434 participants (47%) were men. The proportion receiving a referral for PSD was significantly greater in the novel (8.3%) compared with the traditional (3.6%) eye camp (P < 0.001; risk ratio, 2.31; 95% confidence interval, 2.30-2.34). Among the 183 participants referred from the camps for PSD, 73 (39.9%) followed up for further evaluation at the base hospital.

CONCLUSIONS

In a resource-constrained setting, use of digital fundus photography in novel eye camps resulted in increased detection of and referral for PSD. Further research is needed to determine whether this intervention is cost effective and may contribute to prevention of avoidable blindness and visual impairment in South India. Further research also is needed to improve follow-up of patients referred from camps for suspicion of PSD.

A review of microscopic analysis of blood cells for disease detection with AI perspective

BACKGROUND

Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment.

METHODOLOGY

A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study.

RESULTS

Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made.

CONCLUSION

There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

An airport operations proposal for a pandemic-free air travel

The aviation industry needs to work on the resilience of air travel against health threats and regain passenger trust. This paper proposes a pandemic-free travel concept based on creating an infectious diseases free zone in the airport terminal building through screening of passengers, crews and airport workers. This research shows that infectious disease detection methods applicable at the airport could be available in a short timeframe, at affordable cost and in scale. The potential location of passenger health screening, facilitation requirements, health responsibilities delegation and appropriate usage of industry standards for regulations are key elements to a potential implementation that would be phased and long term.

Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are underexplored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc.) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

Application of machine learning to improve dairy farm management: A systematic literature review

In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.

Bovine leukemia virus detection and dynamics following experimental inoculation

Bovine leukemia virus (BLV) infects more than 40% of the United States cattle population and impacts animal health and production. Control programs aiming to reduce disease prevalence and incidence depend on the ability to detect the BLV provirus, anti-BLV antibodies, and differences in blood lymphocyte counts following infection. These disease parameters also can be indicative of long-term disease progression. The objectives of this study were to determine the timing and to describe early fluctuations of BLV-detection by qPCR, ELISA, and lymphocyte counts. Fifteen Holstein steers were experimentally inoculated with 100 μL of a blood saline inoculum. Three steers served as in-pen negative controls and were housed with the experimentally infected steers to observe the potential for contract transmission. Five additional negative controls were housed separately. Steers were followed for 147 days post-inoculation (DPI). Infections were detected in experimentally infected steers by qPCR and ELISA an average of 24- and 36 DPI, respectively. Significant differences in lymphocyte counts between experimentally infected and control steers were observed from 30 to 45 DPI. Furthermore, a wide variation in peak proviral load and establishment was observed between experimentally infected steers. The results of this study can be used to inform control programs focused on the detection and removal of infectious cattle.

Nanotechnological interventions for plant health improvement and sustainable agriculture

Agriculture is the source of food for both humans and animals. With the growing population demands, agricultural production needs to be scaled up where nanotechnology can play a significant role. The use of nanotechnology in agriculture can manage plant disease and growth for better and quality output. Therefore, this review focuses on the use of various nanoparticles for detection of nutrients and contaminants, nanosensors for monitoring the environmental stresses and crop conditions as well as the use of nanotechnology for plant pathogen detection and crop protection. In addition, the delivery of plant growth regulators and agrichemicals like nanopesticides and nanofertilizers to the plants along with the delivery of DNA for targeted genetic engineering and production of genetically modified (GM) crops are discussed briefly. Further, the future concerns regarding the use of nanoparticles and their possible toxicity, impact on the agriculture and ecosystem needs to be assessed along with the assessment of the nanoparticles and GM crops on the environment and human health.

Analytical characterization of trace elements (zinc, copper, cadmium, lead and selenium) in saliva of pigs under common pathological conditions in the field: a pilot study

Background

This study is focused on the measurement of trace elements (zinc, copper, cadmium, lead and selenium) in the saliva of pigs in order to study their levels on different porcine pathological conditions in the field. The experiment involved 15 pigs without clinical signs of disease and 42 diseased pigs (suffering from lameness, rectal prolapse, fatigue or growth rate retardation). Individual saliva samples were collected, allowing the pigs to chew a sponge each for trace element quantifications through atomic absorption spectrometry (AAS). Since this is the first report on the measurements of trace elements in porcine saliva, a routine analytical validation study was performed for the quantification of all the studied elements. Moreover, the acute phase proteins C-reactive protein (CRP) and haptoblobin (Hp), the total antioxidant capacity (TAC) and adenosine deaminase (ADA) were quantified in the saliva samples for the animal’s health status assessment.

Results

Modifications in the levels of acute phase proteins or ADA were only recorded in animals with lameness and rectal prolapse and those with fatigue respectively. Moreover, TAC level changes were observed in pigs with growth-rate retardation. However, alterations in the levels of two or more trace elements were reported for all the different groups of diseased pigs with evident variations within pathologies.

Conclusions

The salivary quantification of trace elements could be considered as a complementary tool to acute phase proteins, TAC and ADA determinations for disease detection and differentiation in the pig and should be explored in greater depth.

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

BACKGROUND

Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging.

RESULTS

Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult.

CONCLUSIONS

In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer deaths worldwide, and its early detection is a critical determinant of whether curative treatment is achievable. Early stage HCC is typically asymptomatic. Thus, screening programmes are used for cancer detection in patients at risk of tumour development. Radiological screening methods are limited by imperfect data, cost and associated risks, and additionally are unable to detect lesions until they have grown to a certain size. Therefore, some screening programmes use additional blood/serum biomarkers to help identify individuals in whom to target diagnostic cancer investigations. The GALAD score, combining the levels of several blood biomarkers, age and sex, has been developed to identify patients with early HCC. Here we propose a Bayesian hierarchical model for an individual's longitudinal GALAD scores whilst in HCC surveillance to identify potentially significant changes in the trend of the GALAD score, indicating the development of HCC, aiming to improve early detection compared to standard methods. An absorbent two-state continuous-time hidden Markov model is developed for the individual level longitudinal data where the states correspond to the presence/absence of HCC. The model is additionally informed by the information on the diagnosis by standard clinical practice, taking into account that HCC can be present before the actual diagnosis so that there may be false negatives within the diagnosis data. We fit the model to a Japanese cohort of patients undergoing HCC surveillance and show that the detection capability of this proposal is greater than using a fixed cut-point.

Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape

BACKGROUND

The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance.

RESULTS

The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation.

CONCLUSIONS

The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.

Can urinary biomarkers replace cystoscopy?

PURPOSE

Diagnosis and follow-up in patients with non-muscle invasive bladder cancer (NMIBC) rely on cystoscopy and urine cytology. The aim of this review paper is to give an update on urinary biomarkers and their diagnosis and surveillance potential. Besides FDA-approved markers, recent approaches like DNA methylation assays, mRNA gene expression assays and cell-free DNA (cfDNA) are evaluated to assess whether replacing cystoscopy with urine markers is a potential scenario for the future.

METHODS

We performed a non-systematic review of current literature without time period restriction using the National Library of Medicine database ( http://ww.pubmed.gov ). The search included the following key words in different combinations: "urothelial carcinoma", "urinary marker", "hematuria", "cytology" and "bladder cancer". Further, references were extracted from identified articles. The results were evaluated regarding their clinical relevance and study quality.

RESULTS

Currently, replacing cystoscopy with available urine markers is not recommended by international guidelines. For FDA-approved markers, prospective randomized trials are lacking. Newer approaches focusing on molecular, genomic and transcriptomic aberrations are promising with good accuracies. Furthermore, these assays may provide additional molecular information to guide individualized surveillance strategies and therapy. Currently ongoing prospective trials will determine if cystoscopy reduction is feasible.

CONCLUSION

Urinary markers represent a non-invasive approach for molecular characterization of the disease. Although fully replacing cystoscopy seems unrealistic in the near future, enhancing the current gold standard by additional molecular information is feasible. A reliable classification and differentiation between aggressive and nonaggressive tumors by applying DNA, mRNA, and cfDNA assays may change surveillance to help reduce cystoscopies.

The use of infrared thermography for detecting digital dermatitis in dairy cattle: What is the best measure of temperature and foot location to use?

Lameness in dairy cattle is a persistent problem, indicating pain caused by underlying disease states and is associated with reduced milk yields. Digital dermatitis is a common cause of lameness. Thermal imaging is a technique that may facilitate early detection of this disease and has the potential for use in automated detection systems. Previous studies with thermal imaging have imaged either the heels or the coronary band of the foot and typically only used the maximum temperature (Max) value as the outcome measure. This study investigated the utility of other statistical descriptors: 90th percentile (90PCT), 95th percentile (95PCT), standard deviation (SD) and coefficient of variation (CoV) and compared the utility of imaging the heel or coronary band. Images were collected from lame and healthy cows using a high-resolution thermal camera. Analyses were done at the cow and foot level. There were significant differences between lame and healthy feet detectable at the heels (95th percentile: P<0.05; SD: P<0.05) and coronary band (SD: P<0.05). Within lame cows, 95PCT values were higher at the heel (P<0.05) and Max values were higher at the coronary band (P<0.05) in the lame foot compared to the healthy foot. ROC analysis showed an AUC value of 0.72 for Max temperature and 0.68 for 95PCT at the heels. It was concluded that maximum temperature is the most accurate measure, but other statistical descriptors of temperature can be used to detect lameness. These may be useful in certain contexts, such as where there is contamination. Differentiation of lame from healthy feet was most apparent when imaging the heels.

Involvement of metabolites in early defense mechanism of oil palm (Elaeis guineensis Jacq.) against Ganoderma disease

Understanding the mechanism of interaction between the oil palm and its key pathogen, Ganoderma spp. is crucial as the disease caused by this fungal pathogen leads to a major loss of revenue in leading palm oil producing countries in Southeast Asia. Here in this study, we assess the morphological and biochemical changes in Ganoderma disease infected oil palm seedling roots in both resistant and susceptible progenies. Rubber woodblocks fully colonized by G. boninense were applied as a source of inoculum to artificially infect the roots of resistant and susceptible oil palm progenies. Gas chromatography-mass spectrometry was used to measure an array of plant metabolites in 100 resistant and susceptible oil palm seedling roots treated with pathogenic Ganoderma boninense fungus. Statistical effects, univariate and multivariate analyses were used to identify key-Ganoderma disease associated metabolic agitations in both resistant and susceptible oil palm root tissues. Ganoderma disease related defense shifts were characterized based on (i) increased antifungal activity in crude extracts, (ii) increased lipid levels, beta- and gamma-sitosterol particularly in the resistant progeny, (iii) detection of heterocyclic aromatic organic compounds, benzo [h] quinoline, pyridine, pyrimidine (iv) elevation in antioxidants, alpha- and beta-tocopherol (iv) degraded cortical cell wall layers, possibly resulting from fungal hydrolytic enzyme activity needed for initial penetration. The present study suggested that plant metabolites mainly lipids and heterocyclic aromatic organic metabolites could be potentially involved in early oil palm defense mechanism against G. boninense infection, which may also highlight biomarkers for disease detection, treatment, development of resistant variety and monitoring.