Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches.

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.

Adversarial Attacks on Intrusion Detection Systems in In-Vehicle Networks of Connected and Autonomous Vehicles.

Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting focus from the common research on manipulating CAV perception models. Considering the relatively simple nature of IVN data, we assess the susceptibility of IVN-based IDSs to manipulation-a crucial examination, as adversarial attacks typically exploit complexity. We propose an adversarial attack method using a substitute IDS trained with data from the onboard diagnostic port. In conducting these attacks under black-box conditions while adhering to realistic IVN traffic constraints, our method seeks to deceive the IDS into misclassifying both normal-to-malicious and malicious-to-normal cases. Evaluations on two IDS models-a baseline IDS and a state-of-the-art model, MTH-IDS-demonstrated substantial vulnerability, decreasing the F1 scores from 95% to 38% and from 97% to 79%, respectively. Notably, inducing false alarms proved particularly effective as an adversarial strategy, undermining user trust in the defense mechanism. Despite the simplicity of IVN-based IDSs, our findings reveal critical vulnerabilities that could threaten vehicle safety and necessitate careful consideration in the development of IVN-based IDSs and in formulating responses to the IDSs' alarms.

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions.

The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

Orchestration and Management of Adaptive IoT-centric Distributed Applications.

Current Internet of Things (IoT) devices provide a diverse range of functionalities, ranging from measurement and dissemination of sensory data observation, to computation services for real-time data stream processing. In extreme situations such as emergencies, a significant benefit of IoT devices is that they can help gain a more complete situational understanding of the environment. However, this requires the ability to utilize IoT resources while taking into account location, battery life, and other constraints of the underlying edge and IoT devices. A dynamic approach is proposed for orchestration and management of distributed workflow applications using services available in cloud data centers, deployed on servers, or IoT devices at the network edge. Our proposed approach is specifically designed for knowledge-driven business process workflows that are adaptive, interactive, evolvable and emergent. A comprehensive empirical evaluation shows that the proposed approach is effective and resilient to situational changes.

Interaction between people with dysarthria and speech recognition systems: A review.

In recent years, rapid advancements have taken place for automatic speech recognition (ASR) systems and devices. Though ASR technologies have increased, the accessibility of these novel interaction systems is underreported and may present difficulties for people with speech impediments. In this article, we attempt to identify gaps in current research on the interaction between people with dysarthria and ASR systems and devices. We cover the period from 2011, when Siri (the first and the leading commercial voice assistant) was launched, to 2020. The review employs an interaction framework in which each element (user, input, system, and output) contributes to the interaction process. To select the articles for review, we conducted a search of scientific databases and academic journals. A total of 36 studies met the inclusion criteria, which included use of the word error rate (WER) as a measurement for evaluating ASR systems. This review determines that challenges in interacting with ASR systems persist even in light of the most recent commercial technologies. Further, understanding of the entire interaction process remains limited; thus, to improve this interaction, the recent progress of ASR systems must be elucidated.

Automated License Plate Recognition for Resource-Constrained Environments.

The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well.

Dynamically Controlling Offloading Thresholds in Fog Systems.

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security-attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

Blockchain Based Auditable Access Control for Distributed Business Processes.

The use of blockchain technology has been proposed to provide auditable access control for individual resources. However, when all resources are owned by a single organization, such expensive solutions may not be needed. In this work we focus on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains can provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may be overlapping in terms of the component conditions/rules, and simply using existing solutions would result in repeated evaluation of user's authorization separately for each resource, leading to significant overhead in terms of cost and computation time over the blockchain. To address this challenge, we propose an approach that formulates a constraint optimization problem to generate an optimal composite access control policy. This policy is in compliance with all the local access control policies and minimizes the policy evaluation cost over the blockchain. The developed smart contract(s) can then be deployed to the blockchain, and used for access control enforcement. We also discuss how the access control enforcement can be audited using a game-theoretic approach to minimize cost. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and experimentally validated the effectiveness and efficiency of our approach.

Accuracy of American College of Surgeons National Surgical Quality Improvement Program Universal Surgical Risk Calculator in Predicting Complications Following Robot-Assisted Radical Cystectomy at a National Comprehensive Cancer Center.

Introduction: There is paucity of literature about the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) surgical risk calculator for prediction of outcomes after robot-assisted radical cystectomy (RARC). We sought to evaluate the accuracy of the ACS NSQIP surgical risk calculator in the patients who underwent RARC at our institute. Methods: We retrospectively reviewed our prospectively maintained database for patients who underwent RARC between 2005 and 2017. Accuracy of the ACS NSQIP surgical risk calculator was assessed, by comparing the rate of actual complication events after surgery with the receiver operating characteristics curve analysis by calculating the fractional area under the curve (AUC) and the Brier score (BS). We utilized the code number 51595 and 51596 in the ACS NSQIP calculator for the patients undergoing radical cystectomy and reconstructed with the ileal conduit and neobladder, respectively. Results: A total of 462 patients were included in this study: 99 (22%) had diabetes, 302 (66%) had hypertension requiring medication, and 241 (52%) were classified as high American Society of Anesthesiologists (≥3) class. The actual observed rates of any complication and serious complications were 48% and 11%, vs 29% and 25% predicted by the ACS NSQIP, respectively. The actual mean length of hospital stay (10.6 ± 7.8 days) was longer compared with the predicted length (8.5 ± 1.6 days). AUC values were low and the BSs were high for any complication (AUC: 0.50 and BS: 0.29), serious complication (AUC: 0.53 and BS: 0.12), urinary tract infection (AUC: 0.61 and BS: 0.14), renal insufficiency (AUC: 0.64 and BS: 0.08), return to operation room (AUC: 0.58 and BS: 0.07), and early readmission (AUC: 0.55 and BS: 0.11, respectively). Conclusions: The ACS NSQIP calculator demonstrated low accuracy in predicting postoperative outcomes after RARC. These findings highlight the need for development of procedure- and technique-specific RARC calculators.

Natural History and Predictors of Parastomal Hernia after Robot-Assisted Radical Cystectomy and Ileal Conduit Urinary Diversion.

PURPOSE: We investigated the prevalence of and variables associated with parastomal hernia and its outcomes after robot-assisted radical cystectomy and ileal conduit creation for bladder cancer. MATERIALS AND METHODS: We retrospectively reviewed the records of patients who underwent robot-assisted radical cystectomy at our institution. Parastomal hernia was defined as the protrusion of abdominal contents through the stomal defect in the abdominal wall on cross-sectional imaging. Parastomal hernia was further described in terms of patient and hernia characteristics, symptoms, management and outcomes. The Kaplan-Meier method was used to determine time to parastomal hernia and time to surgery. Multivariate stepwise logistic regression was done to evaluate variables associated with parastomal hernia. RESULTS: A total of 383 patients underwent robot-assisted radical cystectomy and ileal conduit creation. Of the patients 75 (20%) had parastomal hernia, which was symptomatic in 23 (31%), and 11 (15%) underwent treatment. Median time to parastomal hernia was 13 months (IQR 9-22). Parastomal hernia developed in 9%, 23% and 32% of cases at 1, 2 and 3 years, respectively. Patients with parastomal hernia had a significantly higher body mass index (30 vs 28 kg/m2, p = 0.02), longer overall operative time (357 vs 340 minutes, p = 0.01) and greater blood loss (325 vs 250 ml, p = 0.04). On multivariate analysis operative time (OR 1.25, 95% CI 1.21-3.90, p <0.001), a fascial defect 30 mm or greater (OR 5.23, 95% CI 2.32-11.8, p <0.001) and a lower postoperative estimated glomerular filtration rate (OR 2.17, 95% CI 1.21-3.90, p = 0.01) were significantly associated with parastomal hernia. CONCLUSIONS: Symptoms develop in approximately a third of patients with parastomal hernia and 15% will require surgery. The risk of parastomal hernia plateaued after postoperative year 3. Longer operative time, a larger fascial defect and lower postoperative kidney function were associated with parastomal hernia.

Detecting deterioration in patients with chronic disease using telemonitoring: navigating the 'trough of disillusionment'.

OBJECTIVES: To examine the evidence base for telemonitoring designed for patients who have chronic obstructive pulmonary disease and heart failure, and to assess whether telemonitoring fulfils the principles of monitoring and is ready for implementation into routine settings. DESIGN: Qualitative data collection using interviews and participation in a multi-path mapping process. PARTICIPANTS: Twenty-six purposively selected informants completed semi-structured interviews and 24 individuals with expertise in the relevant clinical and informatics domains from academia, industry, policy and provider organizations and participated in a multi-path mapping workshop. RESULTS: The evidence base for the effectiveness of telemonitoring is weak and inconsistent, with insufficient cost-effectiveness studies. When considered against an accepted definition of monitoring, telemonitoring is found wanting. Telemonitoring has not been able so far to ensure that the technologies fit into the life world of the patient and into the clinical and organizational milieu of health service delivery systems. CONCLUSIONS: To develop effective telemonitoring for patients with chronic disease, more attention needs to be given to agreeing the central aim of early detection and, to ensure potential implementation, engaging a wide range of stakeholders in the design process, especially patients and clinicians.

Bridging two translation gaps: a new informatics research agenda for telemonitoring of chronic disease.

OBJECTIVE: To propose a research agenda that addresses technological and other knowledge gaps in developing telemonitoring solutions for patients with chronic diseases, with particular focus on detecting deterioration early enough to intervene effectively. DESIGN: A mixed methods approach incorporating literature review, key informant, and focus group interviews to gain an in-depth, multidisciplinary understanding of current approaches, and a roadmapping process to synthesise a research agenda. RESULTS: Counter to intuition, the research agenda for early detection of deterioration in patients with chronic diseases is not only primarily about advances in sensor technology but also much more about the problems of clinical specification, translation, and interfacing. The ultimate aim of telemonitoring is not fully agreed between the actors (patients, clinicians, technologists, and service providers). This leads to unresolved issues such as: (1) How are sensors used by patients as part of daily routines? (2) What are the indicators of early deterioration and how might they be used to trigger alerts? (3) How should alerts lead to appropriate levels of responses across different agencies and sectors? CONCLUSION: Attempts to use telemonitoring to improve the care of patients with chronic diseases over the last two decades have so far failed to lead to systems that are embedded in routine clinical practice. Attempts at implementation have paid insufficient attention to understanding patient and clinical needs and the complex dynamics and accountabilities that arise at the level of service models. A suggested way ahead is to co-design technology and services collaboratively with all stakeholders.

Simultaneous trend analysis for evaluating outcomes in patient-centred health monitoring services.

The research aim underpinning the Healthcare@Home (HH) information system described here was to enable 'near real time' risk analysis for disease early detection and prevention. To this end, we are implementing a family of prototype web services to 'push' or 'pull' individual's health-related data via an system of clinical hubs, mobile communication devices and/or dedicated home-based network computers. We are examining more efficient methods for ethical use of such data in timeline-based (i.e. 'longitudinal') data analysis systems. A consistent data collation infrastructure is being created for use along the 'patient path'--accessible wherever patients happen to be. This 'patient-centred' infrastructure can be applied in the evaluation of disease progression risk (in the light of clinical understanding of disease processes). In this paper we describe the requirements for making multi-data trend management 'scale-up', together with some requirements of an 'end-to-end' functioning data collection system. A Service-Oriented Architecture (SOA) approach is used to maximise benefits from (1) clinical evidence and (2) computational models of disease progression that can be made available elsewhere on the SOA. We discuss the implications of this so-called 'closed loop' approach for improving healthcare intervention outcomes, patient safety, decision support, objective measurement of service quality and in providing inputs for quantitative healthcare (predictive) modelling.

Alchemist multimodal workflows for diabetic retinopathy research, disease prevention and investigational drug discovery.

In this paper we present mechanisms for imaging and spectral data discovery, as applied to the early detection of pathologic mechanisms underlying diabetic retinopathy in research and clinical trial scenarios. We discuss the Alchemist framework, built using a generic peer-to-peer architecture, supporting distributed database queries and complex search algorithms based on workflow. The Alchemist is a domain-independent search mechanism that can be applied to search and data discovery scenarios in many areas. We illustrate Alchemist's ability to perform complex searches composed as a collection of peer-to-peer overlays, Grid-based services and workflows, e.g. applied to image and spectral data discovery, as applied to the early detection and prevention of retinal disease and investigational drug discovery. The Alchemist framework is built on top of decentralised technologies and uses industry standards such as Web services and SOAP for messaging.