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Wang Z, Zhang X, Wang X, Li J, Zhang Y, Zhang T, Xu S, Jiao W, Niu H. Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends. Front Oncol 2023; 13:1152622. [PMID: 37727213 PMCID: PMC10505614 DOI: 10.3389/fonc.2023.1152622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 08/11/2023] [Indexed: 09/21/2023] Open
Abstract
This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.
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Affiliation(s)
- Zijie Wang
- Department of Vascular Intervention, ShengLi Oilfield Center Hospital, Dongying, China
| | - Xiaofei Zhang
- Department of Education and Training, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xinning Wang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianfei Li
- Extenics Specialized Committee, Chinese Association of Artificial Intelligence (ESCCAAI), Beijing, China
| | - Yuhao Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianwei Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shang Xu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Jiao
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haitao Niu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
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Bossung V, Singer A, Ratz T, Rothenbühler M, Leeners B, Kimmich N. Changes in Heart Rate, Heart Rate Variability, Breathing Rate, and Skin Temperature throughout Pregnancy and the Impact of Emotions-A Longitudinal Evaluation Using a Sensor Bracelet. SENSORS (BASEL, SWITZERLAND) 2023; 23:6620. [PMID: 37514915 PMCID: PMC10385491 DOI: 10.3390/s23146620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
(1) Background: Basic vital signs change during normal pregnancy as they reflect the adaptation of maternal physiology. Electronic wearables like fitness bracelets have the potential to provide vital signs continuously in the home environment of pregnant women. (2) Methods: We performed a prospective observational study from November 2019 to November 2020 including healthy pregnant women, who recorded their wrist skin temperature, heart rate, heart rate variability, and breathing rate using an electronic wearable. In addition, eight emotions were assessed weekly using five-point Likert scales. Descriptive statistics and a multivariate model were applied to correlate the physiological parameters with maternal emotions. (3) Results: We analyzed data from 23 women using the electronic wearable during pregnancy. We calculated standard curves for each physiological parameter, which partially differed from the literature. We showed a significant association of several emotions like feeling stressed, tired, or happy with the course of physiological parameters. (4) Conclusions: Our data indicate that electronic wearables are helpful for closely observing vital signs in pregnancy and to establish modern curves for the physiological course of these parameters. In addition to physiological adaptation mechanisms and pregnancy disorders, emotions have the potential to influence the course of physiological parameters in pregnancy.
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Affiliation(s)
- Verena Bossung
- Department of Obstetrics, University Hospital Zurich (USZ), University of Zurich (UZH), 8091 Zurich, Switzerland
| | - Adrian Singer
- Department of Obstetrics, University Hospital Zurich (USZ), University of Zurich (UZH), 8091 Zurich, Switzerland
| | - Tiara Ratz
- Department of Reproductive Endocrinology, University Hospital Zurich (USZ), University of Zurich (UZH), 8091 Zurich, Switzerland
| | | | - Brigitte Leeners
- Department of Reproductive Endocrinology, University Hospital Zurich (USZ), University of Zurich (UZH), 8091 Zurich, Switzerland
| | - Nina Kimmich
- Department of Obstetrics, University Hospital Zurich (USZ), University of Zurich (UZH), 8091 Zurich, Switzerland
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Li X, Ono C, Warita N, Shoji T, Nakagawa T, Usukura H, Yu Z, Takahashi Y, Ichiji K, Sugita N, Kobayashi N, Kikuchi S, Kimura R, Hamaie Y, Hino M, Kunii Y, Murakami K, Ishikuro M, Obara T, Nakamura T, Nagami F, Takai T, Ogishima S, Sugawara J, Hoshiai T, Saito M, Tamiya G, Fuse N, Fujii S, Nakayama M, Kuriyama S, Yamamoto M, Yaegashi N, Homma N, Tomita H. Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability. Front Psychiatry 2023; 14:1104222. [PMID: 37415686 PMCID: PMC10322181 DOI: 10.3389/fpsyt.2023.1104222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/19/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV). Methods Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested. Results and Discussion In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.
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Affiliation(s)
- Xue Li
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Chiaki Ono
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Noriko Warita
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tomoka Shoji
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Takashi Nakagawa
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Hitomi Usukura
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Zhiqian Yu
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Yuta Takahashi
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Kei Ichiji
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Norihiro Sugita
- Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | | | - Saya Kikuchi
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Ryoko Kimura
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yumiko Hamaie
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Mizuki Hino
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Yasuto Kunii
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Keiko Murakami
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Mami Ishikuro
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Taku Obara
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tomohiro Nakamura
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Fuji Nagami
- Department of Public Relations and Planning, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Takako Takai
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Soichi Ogishima
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Junichi Sugawara
- Department of Community Medical Supports, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tetsuro Hoshiai
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masatoshi Saito
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Gen Tamiya
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Nobuo Fuse
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Susumu Fujii
- Department of Disaster Medical Informatics, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Masaharu Nakayama
- Department of Disaster Medical Informatics, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Disaster Public Health, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japan
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Nobuo Yaegashi
- Department of Public Relations and Planning, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyasu Homma
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
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A Model to Predict Heartbeat Rate Using Deep Learning Algorithms. Healthcare (Basel) 2023; 11:healthcare11030330. [PMID: 36766905 PMCID: PMC9914604 DOI: 10.3390/healthcare11030330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
ECG provides critical information in a waveform about the heart's condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.
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An A, Al-Fawa’reh M, Kang JJ. Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm. SENSORS (BASEL, SWITZERLAND) 2022; 22:9679. [PMID: 36560045 PMCID: PMC9787455 DOI: 10.3390/s22249679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/27/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Monitoring a patient's vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device's battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency.
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Affiliation(s)
- Angela An
- School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
- Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
| | - Mohammad Al-Fawa’reh
- Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
| | - James Jin Kang
- Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
- International College, National Taiwan University, Taipei 10617, Taiwan
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D’Onofrio G, Fiorini L, Sorrentino A, Russo S, Ciccone F, Giuliani F, Sancarlo D, Cavallo F. Emotion Recognizing by a Robotic Solution Initiative (EMOTIVE Project). SENSORS 2022; 22:s22082861. [PMID: 35458845 PMCID: PMC9031388 DOI: 10.3390/s22082861] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/03/2022] [Accepted: 04/07/2022] [Indexed: 01/10/2023]
Abstract
Background: Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view. Methods: Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF). Results: According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution. Conclusions: RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.
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Affiliation(s)
- Grazia D’Onofrio
- Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy;
- Correspondence: ; Tel./Fax: +39-0882-410271
| | - Laura Fiorini
- Department of Industrial Engineering, University of Florence, 50121 Florence, Italy; (L.F.); (A.S.); (F.C.)
| | - Alessandra Sorrentino
- Department of Industrial Engineering, University of Florence, 50121 Florence, Italy; (L.F.); (A.S.); (F.C.)
| | - Sergio Russo
- Information and Communication Technology, Innovation & Research Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy; (S.R.); (F.G.)
| | - Filomena Ciccone
- Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Francesco Giuliani
- Information and Communication Technology, Innovation & Research Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy; (S.R.); (F.G.)
| | - Daniele Sancarlo
- Complex Unit of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Filippo Cavallo
- Department of Industrial Engineering, University of Florence, 50121 Florence, Italy; (L.F.); (A.S.); (F.C.)
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