• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4377-4384
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• 2014

According to the World Health Organization (WHO), 1.37 million people died of lung cancer all around the world in 2008, occupying the first place in all cancer-related deaths. However, this number might be decreased if patients were detected earlier and treated appropriately. Unfortunately, traditional imaging techniques are not sufficiently satisfactory for early detection of lung cancer because of limitations. As one alternative, breath volatile organic compounds (VOCs) may reflect the biochemical status of the body and provide clues to some diseases including lung cancer at early stage. Early detection of lung cancer based on breath analysis is becoming more and more valued because it is non-invasive, sensitive, inexpensive and simple. In this review article, we analyze the limitations of traditional imaging techniques in the early detection of lung cancer, illustrate possible mechanisms of the production of VOCs in cancerous cells, present evidence that supports the detection of such disease using breath analysis, and summarize the advances in the study of E-noses based on gas sensitive sensors. In conclusion, the analysis of breath VOCs is a better choice for the early detection of lung cancer compared to imaging techniques. We recommend a more comprehensive technique that integrates the analysis of VOCs and non-VOCs in breath. In addition, VOCs in urine may also be a trend in research on the early detection of lung cancer.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.255-262
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• 2016

Purpose: Fuji apples are one of the top selling exports for South Korea bringing in over $233.4 million in 2013. However, during the last few decades, about half of the Fuji apple orchards have been infected by Apple Marssonina Blotch disease (AMB), a fungal disease caused by Diplocarpon mali., which takes about 40 days to exhibit obvious visible symptoms. Infected leaves turn yellow and begin growing brown lesions. AMB promotes early defoliation and reduces the quality and quantity of apples an infected tree can produce. Currently, there is no prediction model for AMB on the market. Methods: The Precision Agriculture Laboratory (PAL) at the University of Florida (UF) has been working with the National Academy of Agricultural Science, Rural Development Administration, South Korea to investigate the use of hyperspectral data in creating an early detection method for AMB. The RDA has been researching hyperspectral techniques for disease detection at their Apple Research Station in Gunwi since 2012 and disseminates its findings to the local farmers. These farmers were surveyed to assess the state of knowledge of AMB in the area. Out of a population of about 750 growers, 111 surveys were completed (confidence interval of +/- 8.59%, confidence level of 95%, p-value of 0.05). Results: The survey revealed 32% of the farmers did not know what AMB was, but 45% of farmers have had their orchards infected by AMB. Twenty-five percent could not distinguish AMB from other symptoms. Overwhelmingly, 80% of farmers strongly believed an early detection method for AMB was necessary. Conclusions: The results of the survey will help to evaluate the outreach programs of the RDA so they can more effectively educate farmers on the identifying, treating, and mediating AMB.

• Clinical and Experimental Pediatrics
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• v.54 no.5
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• pp.183-191
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• 2011

Although antenatal diagnostic technique has considerably improved, precise detection and proper management of the neonate with congenital heart disease (CHD) is always a great concern to pediatricians. Congenital cardiac malformations vary from benign to serious conditions such as complete transposition of the great arteries (TGA), critical pulmonary and aortic valvular stenosis/atresia, hypoplastic left heart syndrome (HLHS), obstructed total anomalous pulmonary venous return (TAPVR), which the baby needs immediate diagnosis and management for survival. Unfortunately, these life threatening heart diseases may not have obvious evidence early after birth, most of the clinical and physical findings are nonspecific and vague, which makes the diagnosis difficult. High index of suspicion and astute acumen are essential to decision making. When patent ductus arteriosus (PDA) is opened Widely, many serious malformations may not be noticed easily in the early life, but would progress as severe acidosis/shock/cyanosis or even death as PDA constricts after few hours to days. Ductus dependent congenital cardiac lesions can be divided into the ductus dependent systemic or pulmonary disease, but physiologically quite different from each other and treatment strategy has to be tailored to the clinical status and cardiac malformations. Inevitably early presentation is often regarded as a medical emergency. Differential diagnosis with inborn error metabolic disorders, neonatal sepsis, persistent pulmonary hypertension of the newborn (PPHN) and other pulmonary conditions are necessary. Urgent identification of the newborn at such high risk requires timely referral to a pediatric cardiologist, and timely intervention is the key in reducing mortality and morbidity. This following review deals with the clinical presentations, investigative modalities and approach to management of congenital cardiac malformations presenting in the early life.

• BMB Reports
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• v.41 no.10
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• pp.685-692
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• 2008

Colorectal cancer (CRC) is the third most common malignancy in the world. Because CRC develops slowly from removable precancerous lesions, detection of the disease at an early stage during regular health examinations can reduce both the incidence and mortality of the disease. Although sigmoidoscopy offers significant improvements in the detection rate of CRC, its diagnostic value is limited by its high costs and inconvenience. Therefore, there is a compelling need for the identification of noninvasive biomarkers that can enable earlier detection of CRC. Accordingly, many validation studies have been conducted to evaluate genetic, epigenetic or protein markers that can be detected in the stool or in serum. Currently, the fecal-occult blood test is the most widely used method of screening for CRC. However, advances in genomics and proteomics combined with developments in other relevant fields will lead to the discovery of novel non invasive biomarkers whose usefulness will be tested in larger validation studies. Here, non-invasive molecular biomarkers that are currently used in clinical settings and have the potential for use as CRC biomarkers are discussed.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• Journal of Oral Medicine and Pain
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• v.42 no.3
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• pp.85-88
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• 2017

Burkitt's lymphoma is a malignant monoclonal proliferation of early B-lymphocyte. Since Burkitt's lymphoma is a highly aggressive disease, early detection is a crucial. This disease often involves jaw and mandibular mass or swelling may also be seen, but in the early phase of Burkitt's lymphoma these symptoms cannot be observed. A rare case of Burkitt's lymphoma without any mandibular mass and the general symptoms was present. The excruciating toothache led the patient to visit the dental clinic and misdiagnosis of chronic periodontal abscess was made initially. Dentists should consider the oral manifestations of systemic disease when the multiple periodontal ligament space widening is observed and the dental treatment for mimicking odontogenic pain has no effect.

• Journal of Yeungnam Medical Science
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• v.37 no.4
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• pp.341-344
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• 2020

Hereditary neuropathy with liability to pressure palsy (HNPP) is a rare neurological genetic disease caused by deletion of the peripheral myelin protein 22 gene and presents in childhood or young adulthood. We report four cases of HNPP with typical and rare presentations, reflecting the broad clinical spectrum of this disease. Two patients presented with mononeuropathies that are frequently observed in HNPP; the remaining two presented with bilateral neuropathy or mononeuropathy anatomically present in the deep layer. This reflects the broad clinical presentation of HNPP, and clinicians should differentiate these conditions in young patients with monoparesis or bilateral paresis. Although HNPP is currently untreatable, early diagnosis in the emergency department can lead to early detection, eventually resulting in less provocation and recurrence which may cause early motor nerve degeneration.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.