A Non-invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-c Ratio Using Machine learning

2021 ◽  
Author(s):  
Madam Chakradar ◽  
Alok Aggarwal ◽  
Xiaochun Cheng ◽  
Anuj Rani ◽  
Manoj Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Invasive Approach ◽  
Non Invasive

scholarly journals Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Adnan Zahid ◽  
Hasan T. Abbas ◽  
Aifeng Ren ◽  
Ahmed Zoha ◽  
Hadi Heidari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Content ◽  
Operating Time ◽  
Computational Time ◽  
Support Vector ◽  
Terahertz Waves ◽  
Living Plant ◽  
Invasive Approach ◽  
Non Invasive ◽  
Precise Estimation

Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

scholarly journals Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8336
Author(s):  
Hafeez Ur Rehman Siddiqui ◽  
Hina Fatima Shahzad ◽  
Adil Ali Saleem ◽  
Abdul Baqi Khan Khan Khakwani ◽  
Furqan Rustam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Impulse Radio ◽  
Physiological Signals ◽  
Invasive Approach ◽  
Male And Female ◽  
Non Invasive ◽  
Video Clips ◽  
Uwb Radar ◽  
Body Sensors

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

scholarly journals A model ecosystem of twelve cryopreservable microbial species allowing for a non-invasive approach

2020 ◽  
Author(s):  
Kazufumi Hosoda ◽  
Shigeto Seno ◽  
Naomi Murakami ◽  
Hideo Matsuda ◽  
Yutaka Osada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Model ◽  
High Throughput ◽  
Learning Model ◽  
Invasive Approach ◽  
Model Ecosystem ◽  
Non Invasive ◽  
Microbial Species ◽  
Machine Learning Model ◽  
Diverse Interactions

AbstractWe developed a synthetic ecosystem of 12 cryopreservable microbial species with diverse interactions as an experimental “model ecosystem.” We created a machine learning model that noninvasively distinguished the 12 species on micrographs enabling high-throughput measurements. Our synthetic ecosystems maintained a certain diversity for at least six months.

scholarly journals Salivary cortisol as a non-invasive approach to assess stress in dystocic dairy calves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Levente Kovács ◽  
Fruzsina Luca Kézér ◽  
Szilárd Bodó ◽  
Ferenc Ruff ◽  
Rupert Palme ◽  
...  
Keyword(s):  
Birth Weight ◽  
Body Condition ◽  
Salivary Cortisol ◽  
Sampling Period ◽  
Dairy Calves ◽  
Birth Process ◽  
Invasive Approach ◽  
Non Invasive ◽  
Saliva Cortisol ◽  
Cortisol Levels

AbstractThe intensity and the magnitude of saliva cortisol responses were investigated during the first 48 h following birth in newborn dairy calves which underwent normal (eutocic, EUT, n = 88) and difficult (dystocic, DYS, n = 70) calvings. The effects of parity and body condition of the dam, the duration of parturition, the time spent licking the calf, the sex and birth weight of the calf were also analyzed. Neonatal salivary cortisol concentrations were influenced neither by factors related to the dam (parity, body condition) nor the calf (sex, birth weight). The duration of parturition and the time spent licking the calf also had no effect on salivary cortisol levels. Salivary cortisol concentrations increased rapidly after delivery in both groups to reach their peak levels at 45 and 60 min after delivery in EUT and DYS calves, respectively supporting that the birth process means considerable stress for calves and the immediate postnatal period also appears to be stressful for newborn calves. DYS calves exhibited higher salivary cortisol concentrations compared to EUT ones for 0 (P = 0.022), 15 (P = 0.016), 30 (P = 0.007), 45 (P = 0.003), 60 (P = 0.001) and 120 min (P = 0.001), and for 24 h (P = 0.040), respectively. Peak levels of salivary cortisol and the cortisol release into saliva calculated as AUC were higher in DYS than in EUT calves for the 48-h of the sampling period (P = 0.009 and P = 0.003, respectively). The greater magnitude of saliva cortisol levels in DYS calves compared to EUT ones suggest that difficult parturition means severe stress for bovine neonates and salivary cortisol could be an opportunity for non-invasive assessment of stress during the early neonatal period in cattle.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Bote ◽  
J F Ortega-Morán ◽  
C L Saratxaga ◽  
B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Structural Information ◽  
Ex Vivo ◽  
Ground Truth ◽  
Colon Polyps ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

scholarly journals Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2469
Author(s):  
Chen-Yi Xie ◽  
Chun-Lap Pang ◽  
Benjamin Chan ◽  
Emily Yuen-Yuen Wong ◽  
Qi Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Imaging ◽  
Risk Groups ◽  
Review Of Literature ◽  
Primary Tumors ◽  
Non Invasive ◽  
Significant Difference ◽  
Recent Developments ◽  
Health Significance ◽  
Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

scholarly journals The Cat Mandible (II): Manipulation of the Jaw, with a New Prosthesis Proposal, to Avoid Iatrogenic Complications

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 683
Author(s):  
Matilde Lombardero ◽  
Mario López-Lombardero ◽  
Diana Alonso-Peñarando ◽  
María del Mar Yllera
Keyword(s):  
Imaging Techniques ◽  
Recovery Period ◽  
Prosthesis Design ◽  
Patient Specific ◽  
Mandibular Fractures ◽  
Rigid Fixation ◽  
Invasive Approach ◽  
Non Invasive ◽  
Iatrogenic Complications ◽  
Mandibular Body

The cat mandible is relatively small, and its manipulation implies the use of fixing methods and different repair techniques according to its small size to keep its biomechanical functionality intact. Attempts to fix dislocations of the temporomandibular joint should be primarily performed by non-invasive techniques (repositioning the bones and immobilisation), although when this is not possible, a surgical method should be used. Regarding mandibular fractures, these are usually concurrent with other traumatic injuries that, if serious, should be treated first. A non-invasive approach should also first be considered to fix mandibular fractures. When this is impractical, internal rigid fixation methods, such as osteosynthesis plates, should be used. However, it should be taken into account that in the cat mandible, dental roots and the mandibular canal structures occupy most of the volume of the mandibular body, a fact that makes it challenging to apply a plate with fixed screw positions without invading dental roots or neurovascular structures. Therefore, we propose a new prosthesis design that will provide acceptable rigid biomechanical stabilisation, but avoid dental root and neurovascular damage, when fixing simple mandibular body fractures. Future trends will include the use of better diagnostic imaging techniques, a patient-specific prosthesis design and the use of more biocompatible materials to minimise the patient’s recovery period and suffering.

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