A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis

Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The gold standard\u27\u27 for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning

Application of artificial intelligence techniques for brain-computer interface in mental fatigue detection: a systematic review (2011-2022)

Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies

Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)

Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

Diagnostic tool for detection of cancer

Cancer is a class of disease or disorders. Basically, it is categorized into three main types; carcinoma (epithelial cancer), sarcoma (cancer of connective tissues or muscle cells), and leukemia (cancer of hemopoietic cells). Most of cancers originate from abnormal cells that divide without control. It begins with a genetic defect resulting in mutagens that create abnormal cells. Subsequently, cancerous growth occurs due to defective control of cell death and proliferation. At the end, metastasis – a state whereby cancer cells can invade nearby tissues and can spread through the bloodstream and lymphatic system to other parts of the body – takes place as a weapon for mass destruction. Many studies have been made over the past several decades in elucidating the genetic and biochemical changes that underline the development of cancer. One of the objectives for such research is that having better understanding of causes of cancer will lead to improved strategies for cancer diagnosis, thereby prevent cancer fatalities. The current screening approaches include; biopsy (process of removing a piece of tissue for microscopic examination), optical imaging, ultrasonography (diagnosis process using ultrasound, for example MRI), and blood test (examination of abnormalities in level of hormone, metabolites, nucleosides, and proteins) have shown a potential diagnosis capability to detect cancer.RG36/0

Biodegradable ocular implants

Cytomegalovirus (CMV) retinitis is the most common ocular infection in patients with Acquired Immune Deficiency Syndrome (AIDS), occurring in 15%-40% of AIDS patients. It will cause blindness in 3 to 6 months if left untreated. The available mode of treatments ranges from intravenous to implants for localised drug delivery. However, several problems have been associated with these treatments and a sustained-release biodegradable device is needed to decrease these risks and produce a longer therapeutic effect

Novel biomaterials

This report consists of 3 projects: Project 1: Nano and microparticles for drug delivery Part 1 - At present stage, we have developed a PLGA microparticle system which has a particle size range of 3-8 micron. It has the particle size especially suitable for intravenous administration. which is desired to prevent capillary clogging and phagocytosis. Part 2 - In this project, poly (lactic-co-glycolic acid 75:25, PLGA), IV 0.94 dl/g, was chosen as the matrix of the microparticles. Bovine serum albumin (BSA) (Fraction V) as the model drug was incorporated in the microparticles by a w/o/w emulsification and solvent evaporation technique. The relationships between the various preparation parameters and the size, morphology and drug loading efficiency and drug release profiles of the resultant microparticles were examined. Project 2: Plasma Spray processing of functionally graded bioceramic nano-composites The project was to find a bioceramic based composite with suitable mechanical properties and biological properties so that the composite material would be used to manufacture load-bearing implants for bone replacement applications. Project 3: In-vitro and in-vivo evaluation of biomaterials The project was designed to evaluate the biocompatibility, osteointegration and osteoinduction of biomaterials in vivo and in vitro

5-Fluorouracil encapsulated HA/PLGA composite microspheres for cancer therapy

5-Fluorouracil (5FU) was successfully entrapped within poly(lactide-co-glycolide) (PLGA) and hydroyapatite (HA) composite microspheres using the emulsification/solvent extraction technique. The effects of HA to PLGA ratio, solvent ratio as well as polymer inherent viscosity (IV) on encapsulation efficiency were investigated. The degradation and drug release rates of the microspheres were studied for 5 weeks in vitro in phosphate buffered solution of pH 7.4 at 37 °C. The drug release profile followed a biphasic pattern with a small initial burst followed by a zero-order release for up to 35 days. The initial burst release decreased with increasing HA content. The potential of HA in limiting the initial burst release makes the incorporation of HA into PLGA microspheres advantageous since it reduces the risk of drug overdose from high initial bursts. The linear sustained drug release profile over the course of 5 weeks makes these 5-FU-loaded HA/PLGA composite microparticles a promising delivery system for the controlled release of chemotherapy drugs in the treatment of cancer

Fabrication of cisplatin-loaded poly(lactide-co-glycolide) composite microspheres for osteosarcoma treatment

Purpose To reduce the toxicity and achieve a sustainable and controllable release of cisplatin (CDDP). Methods CDDP was loaded onto Fe5 (Fe3+ doped hydroxyapatite at atomic ratio of Feadded/Caadded = 5%) nanoparticles through surface adsorption. Subsequently, CDDP-loaded Fe5 nanoparticles (CDDP-Fe5) and/or CDDP were encapsulated into poly(lactide-co-glycolide) (PLGA) microspheres using oil-in-water single emulsion. Drug release profiles and degradation behaviors were monitored. Results CDDP-Fe5 demonstrated a high initial burst (42% on day 1) and short release time (25 days) as CDDP was directly released from Fe5 nanoparticles. CDDP-Fe5 encapsulated within the PLGA microspheres revealed a lower initial burst (23% on day 1) and longer release time (55 days) than CDDP-Fe5. Compared with PLGA microspheres containing only CDDP, which showed typical biphasic release manner, microspheres with CDDP-Fe5 and CDDP demonstrated a nearly linear release after the initial burst. Fe5 and CDDP delayed microsphere degradation. All samples became porous, disintegrated, fused, and formed pellets at the end of the study. Conclusion Fe5/PLGA composite microspheres showed favorable CDDP release behavior compared to microspheres composed of polymer alone, suggesting its potential as a new CDDP formulation