Hyein Jeong¹, Soobin Jang², Bo-Hyoung Jang¹, Chunhoo Cheon¹, Taek Gyu Kim³, Chan Ho Ju³, Hwimun Kim⁴, Su Yong Shin⁵, Kyeong Han Kim^6,*, Seong-Gyu Ko^1,*

International Journal of Mental Health Promotion, Vol.27, No.11, pp. 1695-1708, 2025, DOI:10.32604/ijmhp.2025.071014 - 28 November 2025

Abstract Objectives: This study aimed to evaluate the clinical effectiveness, durability, and acceptability of a Korean medicine-based online mental health self-care program for mothers. Methods: This non-randomized comparative study evaluated the clinical effectiveness, durability, and acceptability of a Korean medicine-based online mental health self-care program for mothers. Group 1 (regular version) included 120 participants who attended one live session per week for 5 weeks, while Group 2 (shortened version) included 30 participants who completed five recorded sessions within 1 week. A total of 112 participants (93.3%) in Group 1 and all 30 participants (100%) in Group 2… More >

Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Nicholas Sellke^1,2,*, Erin Jesse^1,2, Justin M. Dubin³, Tomislav D. Medved^1,2, Neha S. Basti⁴, Janvi Ramchandra², Robert E. Brannigan⁴, Joshua A. Halpern⁴, Nannan Thirumavalavan^1,2

Canadian Journal of Urology, Vol.32, No.2, pp. 89-94, 2025, DOI:10.32604/cju.2025.065004 - 30 April 2025

Abstract Introduction: Direct-to-consumer (DTC) telemedicine has emerged as an option for transgender patients seeking gender affirming hormone therapy (GAHT). We aimed to characterize the healthcare services provided by DTC telemedicine companies offering GAHT and to compare their costs to a tertiary care center. Methods: We identified DTC telemedicine platforms offering GAHT via internet searches and extracted information from their websites related to evaluation, treatment, monitoring, and cost. Cost of the DTC GAHT was compared to cost for comparable services at a tertiary care center. Results: Six DTC companies were identified. All platforms utilized an informed consent model… More >

Aziz Alotaibi^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3741-3771, 2025, DOI:10.32604/cmc.2025.061998 - 06 March 2025

Abstract Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data. Recently, both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions. With the growth in popularity of deep learning and ensemble learning algorithms, they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data. Ensemble deep learning has exhibited significant performance in enhancing learning generalization through… More >

Attiya Khan¹, Muhammad Rizwan², Ovidiu Bagdasar^2,3, Abdulatif Alabdulatif^4,*, Sulaiman Alamro⁴, Abdullah Alnajim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2121-2141, 2024, DOI:10.32604/cmes.2024.054380 - 31 October 2024

Abstract The Internet of Medical Things (IoMT) is an emerging technology that combines the Internet of Things (IoT) into the healthcare sector, which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs. As IoMT devices become more scalable, Smart Healthcare Systems (SHS) have become increasingly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security. An IDS monitors systems or networks for suspicious activities or potential threats, safeguarding internal networks. This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets. We propose More >

K. Akilandeswari¹, Nithya Rekha Sivakumar^2,*, Hend Khalid Alkahtani³, Shakila Basheer³, Sara Abdelwahab Ghorashi²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1189-1205, 2024, DOI:10.32604/cmc.2023.034815 - 30 January 2024

Abstract In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression… More >

J. Ryan Mark¹, Leonard G. Gomella¹, Costas D. Lallas¹, Katherine E. Smentkowski¹, Anne Calvaresi¹, Nathan Handley², Robert B. Den³, Patrick Mille², William J. Tester², Jean Hoffman-Censits⁴, Adam P. Dicker³, Edward Klonicke¹, Ethan Halpern⁵, Peter McCue⁵, W. Kevin Kelly², Edouard J. Trabulsi⁶

Canadian Journal of Urology, Vol.30, No.3, pp. 11526-11531, 2023

Abstract Introduction: We report the impact of our 25-year multidisciplinary care delivery model experience on patients with muscle invasive bladder cancer treated at our National Cancer Institute (NCI)-designated Sidney Kimmel Cancer Center at Jefferson University. To our knowledge, our multidisciplinary genitourinary cancer clinic (MDC) is the longest continuously operating center of its kind at an NCI Cancer Center in the United States.
Materials and methods: We selected a recent group of patients with cT2-4 N0-1 M0 bladder cancer seen in the Sidney Kimmel Cancer Center Genitourinary Oncology MDC from January 2016 to September 2019. These patients were identified… More >

Logan G. Briggs^1,*, Nishant Uppal^2,*, Björn Langbein³, Naeem Bhojani⁴, Martin Kathrins³, Quoc-Dien Trinh³

Canadian Journal of Urology, Vol.30, No.5, pp. 11659-11667, 2023

Abstract Introduction: To characterize venture capital (VC) investments in urology in the past decade that represent promising innovations in early-stage companies.
Materials and methods: A retrospective analysis of deals made between VC investors and urologic companies from January 1, 2011, through June 28, 2021, was conducted by using a financial database (PitchBook Platform, PitchBook Data Inc). Data on urologic company and investor names; company information and funding categories (surgical device, therapeutic device, drug discovery/pharmaceutical, and health care technology companies); and deal sizes (in US dollars) and dates were abstracted and aggregated. Descriptive and linear regression analyses were conducted.
… More >

J. Prabakaran^1,*, P. Selvaraj²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2141-2157, 2023, DOI:10.32604/csse.2023.034210 - 09 February 2023

Abstract In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into… More >

Ji-Won Baek¹, Kyungyong Chung^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6573-6586, 2023, DOI:10.32604/cmc.2023.035246 - 28 December 2022

Abstract Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP.… More >