People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.

An efficient product packing system through a simplified automatic counting and collecting process is a critical aspect of the manufacturing industry. In the present paper, we propose a real-time tool for simultaneous counting and collection of the objects into different bins using machine vision. The system employs a minimum distance classifier for object detection, Counting on the conveyor belt is done by tracking the Euclidian distances between the centroids of the objects in successive frames. Furthermore, the required number of object collection is carried out in two steps. In the first step, multiple objects nearest to the required count are collected. In the second step, the difference of objects that remained from the previous step are added one by one. Object collection is carried out by integrating software output to the hardware, which collects them in required numbers in the different bins. To meet the fast counting requirements, a high-speed conveyor system constituting vision sensor with pixel resolution of 1920x1200 and 41 frames per second frame rate is employed. The high computational power requirements for implementing the algorithm is accomplished by employing a dedicated graphics microcontroller, whose GPIO pins are used for interfacing the hardware for the object collection. The testing results of the proposed device with 30 variants of different plumbing industry objects confirm an accuracy of 100% for a period of 3 hours. Moreover, the proposed module is scalable and could boost the productivity of the company in a complex environment without affecting the accuracy of the system.

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channel SpO ₂ signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA using SpO ₂ signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL for SpO ₂ signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility of SpO ₂ signals in wearable devices for home-based SA detection.

It is essential to have enough sleep for a healthy life; otherwise, it may lead to sleep disorders such as apnea, narcolepsy, insomnia, and periodic leg movements. A polysomnogram (PSG) is typically used to analyze sleep and identify different sleep disorders. This work proposes a novel convolutional neural network (CNN)-based technique for insomnia detection using single-channel electroencephalogram (EEG) signals instead of complex PSG. Morlet wavelet-based continuous wavelet transforms and smoothed pseudo-Wigner-Ville distribution (SPWVD) are explored in the proposed method to obtain scalograms of EEG signals of duration 1s along with convolutional layers for features extraction and image classification. The Morlet transform is found to be a better time-frequency distribution. We have developed Morlet wavelet-based CNN (MWTCNNet) for the classification of healthy and insomniac patients using cyclic alternating pattern (CAP) and sleep disorder research centre (SDRC) databases with C4-A1 single-channel EEG derivation. We have used multiple cohorts/settings of the CAP and SDRC databases to analyse the performance of proposed model. The proposed MWTCNNet achieved an accuracy, sensitivity, and specificity of 98.9%, 99.03%, and 98.66%, respectively, using the CAP database, and 99.03%, 99.20%, and 98.87%, respectively, with the SDRC database. Our proposed model performs better than existing state-of-the-art models and can be tested on a vast, diverse database before being installed for clinical application.

Background. Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods. We followed the recommendations given in the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a selection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various methods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intelligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this detection task. Results. The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential components under investigation in the automated technique include the data input source, objective, ML and DL network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion. Based on our review of the studies featured in this paper, we have identified a notable research gap in the current methods for identifying insomnia and opportunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.