Computer-Aided Detection, Pulmonary Embolism, Computerized Tomography Pulmonary Angiography: Current Status

Angiography (mostly computed tomography, but in some cases, conventional) is still the gold diagnostic standard in the clinical diagnosis of pulmonary embolism (PE). Computer-aided detection (CAD) is software that alerts radiologists the presence of PE during computerized tomography pulmonary angiography (CTPA) examinations. Interpreting CTPA scans with the aid of commercially available CTPA-CAD has improved the detectability of PE patients. This chapter aims to complete the scope of this book by explaining the clinical evidences of PE, the CTPA technology, the role of CTPA-CAD software in improving the diagnostic abilities of CTPA and the role of conventional pulmonary angiography in daily clinical practice. The reader will be introduced to the performance of diagnosing PE with or without the aid of CTPA-CAD algorithms. Differences among CTPA-CAD’s output will be compared and tabled according to “per patient,” “per clot,” “first reader,” and “second reader” basis. This includes, but not limited to, the CTPA-CAD’s sensitivity and specificity in comparison to human observer performance (i.e., radiologist). These topics cover the current status practice at the pulmonary angiography clinic

An Energy-Autonomous Smart Shirt employing wearable sensors for Users’ Safety and Protection in Hazardous Workplaces

none4siWearable devices represent a versatile technology in the IoT paradigm, enabling noninvasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where aWiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.Special Issue “Innovative Materials, Smart Sensors and IoT-based Electronic Solutions for Wearable Applications”, https://www.mdpi.com/journal/applsci/special_issues/Materials_Sensors_Electronic_Solutions_Wearable_ApplicationsopenRoberto De Fazio, Abdel-Razzak Al-Hinnawi, Massimo De Vittorio, Paolo ViscontiDE FAZIO, Roberto; Al-Hinnawi, Abdel-Razzak; DE VITTORIO, Massimo; Visconti, Paol

Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza

Reconstruction and Visualization of 5&#x03BC;m Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

A Computerized Medical Image Processing (CMIP) method is proposed to address the current challenges of optical coherence tomography angiography (OCTA): 1) the need for observing the macula vasculature concerning natural curvature of the macula region; 2) the need for generating OCTA frames at successive small depths in all macula layers; and 3) the need for enhancing the visibility of blood vessels, particularly below the outer retina region. The proposed CMIP method involves image preprocessing, reconstruction, and enhancement stages. Twenty subjects were obtained from the OCTA500 dataset, which was obtained from the OptoVue OCTA machine. The 20 subjects comprise the two OCTA fields of view (FOV), right and left eyes (OD and OS), and five common macula disorders. The sequential enface OCTA images at $5\mu \text{m}$ macula depths were displayed. The presentation of the macula vasculature was enhanced at all depths. The resulting new ophthalmic views enable: 1) avoiding the superimposition of macula vasculature into a projection map; 2) enhancing the OCTA presentation of blood vessels; and 3) inspecting the macula&#x2019;s 3D oval-shaped. The proposed CMIP method can generate sectional macula coronal views (MCV) for every $5\mu \text{m}$ depth, clarifying the macula&#x2019;s curvature in a way that has not been presented in OCTA machines. Therefore, &#x201C;tracking&#x201D; the 3D propagation of the blood vessel network at all macula depths becomes possible. Furthermore, the blood vessels&#x2019; display at all macula depths, including the deep choroid layers, is enhanced. The method yields futuristic ophthalmic advantages that would allow the physician to precisely inspect the 3D localization and diffusion of the macula disorders. The method is invariant to the OCTA&#x2019;s FOVs, macula disorder, and OD or OS eye

Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test's diagnosis using ATS and ERS Respiratory Standard Parameters

The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying the pulmonary function tests into normal, obstructive, restrictive, or mixed cases. The 23 respiratory parameters specified by the ATS and the ERS guidelines, obtained from the Pulmonary Function Test (PFT) device, were employed as input features to a Multi-Layer Perceptron (MLP) neural network. Thirteen possible MLP Back Propagation (BP) algorithms were assessed. Three different categories of respiratory diseases were evaluated, namely obstructive, restrictive, and mixed conditions. The framework was applied on 201 PFT examinations: 103 normal and 98 abnormal cases. The PFT decision support system’s outcomes were compared with both the clinical truth (physician decision) and the PFT built-in diagnostic software. It yielded 92–99% and 87–92% accuracies on the training and the test sets, respectively. An 88–94% area under the receiver operating characteristic curve (ROC) was recorded on the test set. The system exceeded the performance of the PFT machine by 9%. All 23 ATSnERS standard PFT parameters can be used as inputs to design a PFT decision support system, yielding a favorable performance compared with the literature and the PFT machine’s diagnosis program

A Prototype of an Electronic Pegboard Test to Measure Hand-Time Dexterity with Impaired Hand Functionality

This paper proposes an electronic prototype of the Grooved Pegboard Test (GPT), which is normally used to test the presence of hand dexterity. The prototype imitates the geometrical dimensions of an on-the-market GPT device, but it is electronic, not manual like the one available now for users. The suggested electronic GPT device makes automated time calculation between placing the first and the last peg in their designated locations, instead of manually observing a stopwatch normally used during the GPT. The electronic GPT prototype consists of a fabricated wooden box, electronics (switches and microcontroller), and liquid crystal display (LCD). A set of 40 normal volunteers, 20 females and 20 males, tested the designed prototype. A set of six volunteers with chronic medical conditions also participated in evaluating the proposed model. The results on normal volunteers showed that the proposed electronic GPT device yielded time calculations that match the population mean value of similar calculations by the GPT device. The one-sample t-test showed no significant difference in calculations between the new electronic GPT and the manual GPT device. The p-value was much higher than 0.05, indicating the possible use of the suggested electronic GPT device

A Prototype of an Electronic Pegboard Test to Measure Hand-Time Dexterity with Impaired Hand Functionality

This paper proposes an electronic prototype of the Grooved Pegboard Test (GPT), which is normally used to test the presence of hand dexterity. The prototype imitates the geometrical dimensions of an on-the-market GPT device, but it is electronic, not manual like the one available now for users. The suggested electronic GPT device makes automated time calculation between placing the first and the last peg in their designated locations, instead of manually observing a stopwatch normally used during the GPT. The electronic GPT prototype consists of a fabricated wooden box, electronics (switches and microcontroller), and liquid crystal display (LCD). A set of 40 normal volunteers, 20 females and 20 males, tested the designed prototype. A set of six volunteers with chronic medical conditions also participated in evaluating the proposed model. The results on normal volunteers showed that the proposed electronic GPT device yielded time calculations that match the population mean value of similar calculations by the GPT device. The one-sample t-test showed no significant difference in calculations between the new electronic GPT and the manual GPT device. The p-value was much higher than 0.05, indicating the possible use of the suggested electronic GPT device