Early Detection of Avian Diseases Based on Thermography and Artificial Intelligence.

Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions.

Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study.

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist.

Convolution Neural Network for Pain Intensity Assessment from Facial Expression.

Pain is an unpleasant feeling that can reflect a patient's health situation. Since measuring pain is subjective, time-consuming, and needs continuous monitoring, automated pain intensity detection from facial expression holds great potential for smart healthcare applications. Convolutional Neural Networks (CNNs) are recently being used to identify features, map and model pain intensity from facial images, delivering great promise in helping practitioners detect disease.

ATLAS: An Adaptive Transfer Learning Based Pain Assessment System: A Real Life Unsupervised Pain Assessment Solution.

Undertreatment or overtreatment of pain will cause severe consequences physiologically and psychologically. Thus, researchers have made great efforts to develop automatic pain assessment approaches based on physiological signals using machine learning techniques. However, state-of-art research mainly focuses on verifying the hypothesis that physiological signals can be used to assess pain.

Molecular Communication Transmitter Design in Limited-Capacity Storage Regime.

The limited storage capacity at the transmitters of a molecular communication (MC) system can affect the system's performance. One of the reasons for this limitation is the size restriction of the transmitter, which the storage must be replenished so that the transmitter has enough molecules for future transmission. This paper proposes a biologically inspired transmitter model based on neurons for MC whose storage charging and discharging follow differential equations.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features.

Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest.

Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging.

Dental stem cell banking: Techniques and protocols.

Dental tissue-derived stem cells (DSCs) provide an easy, accessible, relatively noninvasive promising source of adult stem cells (ASCs), which brought encouraging prospective for their clinical applications. DSCs provide a perfect opportunity to apply for a patient's own ASC, which poses a low risk of immune rejection. However, problems associated with the long-term culture of stem cells, including loss of proliferation and differentiation capacities, senescence, genetic instability, and the possibility of microbial contamination, make cell banking necessary.

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems.

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection.

Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework.

Objective: The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis.

Author Correction: Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Automatic segmentation of Sperm's parts in microscopic images of human semen smears using concatenated learning approaches.

Accurate segmentation of the sperms in microscopic semen smear images is a prerequisite step in automatic sperm morphology analysis. It is a challenging task due to the non-uniform distribution of light in semen smear images, low contrast between sperm's tail and its surrounding region, the existence of various artifacts, high concentration of sperms and wide spectrum of the shapes of the sperm's parts. This paper proposes an automatic framework based on concatenated learning approaches to segment the external and internal parts of the sperms.

Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography.

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution.

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.

Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced.

Validation of delay-multiply-and-standard-deviation weighting factor for improved photoacoustic imaging of sentinel lymph node.

Delay-and-sum (DAS) is one of the most common algorithms used to construct the photoacoustic images due to its low complexity. However, it results in images with high sidelobes and low resolution. Delay-and-standard-deviation (DASD) weighting factor can improve the contrast of the images compared to DAS.

Photoacoustic image formation based on sparse regularization of minimum variance beamformer.

Delay-and-sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm.

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS.

Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging.

In ultrasound (US) imaging, delay and sum (DAS) is the most common beamformer, but it leads to low-quality images. Delay multiply and sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from the level of side lobes and low noise suppression.

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging.

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals.

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.

Purpose: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans.

Brown adipose tissue does not seem to mediate metabolic adaptation to overfeeding in men.

Objective: Brown adipose tissue (BAT) generates heat in response to cold, and low BAT activity has been linked to obesity. However, recent studies were inconclusive as to whether BAT is involved in diet-induced thermogenesis and mitigates weight gain from prolonged overeating. Therefore, this study investigated whether BAT activity is related to metabolic adaptation arising from 8 weeks of overfeeding in humans.

Evaluation of bone regeneration potential of dental follicle stem cells for treatment of craniofacial defects.

Background Aims: Stem cell-based tissue regeneration offers potential for treatment of craniofacial bone defects. The dental follicle, a loose connective tissue surrounding the unerupted tooth, has been shown to contain progenitor/stem cells. Dental follicle stem cells (DFSCs) have strong osteogenesis capability, which makes them suitable for repairing skeletal defects.

New method for estimation of mobile location based on signal attenuation and hata model signal prediction.

This paper proposes a new algorithm, based on the received signal strength, for mobile location estimation. The algorithm, considering path loss and shadowing for the channel, estimates the distance between the mobile and the home and neighboring base stations, and computes an improved form of the weighted gravity center of the above estimated distances. Simulation results show that the mean error estimation of the mobile location obtained by the proposed algorithm is much smaller than that of the cell-ID, and it is at least 15% better than that of the weighted gravity center method.