Intelligent Software Research Articles

Artificial Intelligence–Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy

PurposeTo validate the sensitivity and specificity of a 3-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection and to establish concordance between AI-generated needle paths and those used in actual biopsy procedures. Materials and MethodsThis was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1–5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories. ResultsThe model achieved an overall AUC of 97.4% (95% CI, 96.3%–98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60). ConclusionsSegmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.

MODERN INTELLECTUAL TECHNOLOGIES IN COMPUTER TOMOGRAPHY

In this article, an approach for the enhancement of computer tomography imaging by integrating modern technologies, including photon counting and dual-source CT, along with the application of the Ai-rad principles in Siemens Computer Tomography systems, is presented. The study proposes and empirically assesses the impact of these technologies on image quality and radiation safety. The architecture of dual-source CT and the photon-counting technique are explored, showcasing their role in improving diagnostic precision. Moreover, the article elaborates on the implementation of artificial intelligence through the Ai-rad approach, aimed at optimizing the scanning process and image analysis, while ensuring radiation doses are kept as low as reasonably achievable. The advantages of these contemporary technologies in addressing current challenges in CT imaging are presented. The results indicate that the synergy between advanced hardware capabilities and intelligent software algorithms leads to enhanced image resolution, reduced exposure to radiation, and overall improved clinical outcomes. This multi-faceted approach exemplifies the potential of modern innovations to set new benchmarks in the field of medical imaging. Keywords: Dual-Source CT, Photon Counting, AI-Rad, myNeedle, Diagnostic Precision, Radiation Safety.

Reinforcement learning tutor better supported lower performers in a math task

Resource limitations make it challenging to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a pivotal tool to decrease the development costs and enhance the effectiveness of intelligent tutoring software, that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.

Development of Surface Roughness Prediction and Monitoring System in Milling Process

High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.

Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems

&lt;p&gt;Intelligent transportation systems (ITS) emphasise the significance of vehicle networks. The growing need for services that are safer, more effective, more affordable, infotainment-focused, and sustainable, however, presents difficulties for these networks. To create innovative applications, researchers and businesses are working. Through effectively coordinating vehicle operations, ITS promotes safe driving, efficient traffic flow, and effective route planning. Referring to automobile heterogeneous, autonomous, flexible, and programmable networks is important given the requirement for convergence of technology in communications. For research and network development, new emerging technologies present intriguing gaps. In this paper, we provide an analysis of wireless technology and potential obstacles to delivering vehicle-to-x communication; including linked cars or autonomous vehicles, which that the initial robot to directly impact the everyday Millions of lived individuals. Study investigates the conceptual change in transportation made possible by the incorporation of modern technology into Intelligent Transportation Systems (ITS), including 5G, heterogeneous networks (HN), and Software Defined Networking (SDN). The incorporation of 5G ensures unparalleled velocity and minimal latency, allowing instantaneous communication between automobiles and infrastructure. Vehicles are easily switched between several network technologies due to heterogeneous networks’ seamless communication structure. Technology developments generated an important increase in the worldwide ITS market from 2018 to 2025. During the same time, the global market for SDN increased significantly, indicating a rising to need for programmable and dynamic network infrastructures. The simultaneous growth patterns in the SDN and ITS industries between 2018 and 2025 indicate to a general shift in the sector toward more intelligence and connectivity. It is predicted that this development continues for future. We pay particular attention to the SDN used in the 5G architecture and how it affects HN.&lt;/p&gt;

Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program.

There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiographic dental pathologies in dogs and cats was assessed for agreement with two human evaluators. Furcation bone loss, periapical lucency, resorptive lesion, retained tooth root, attachment (alveolar bone) loss and tooth fracture were assessed. The AISP does not attempt to diagnose or provide treatment recommendations, nor has it been trained to identify other types of radiographic pathology. Inter-rater reliability for detecting pathologies was measured by absolute percent agreement and Gwet's agreement coefficient. There was good to excellent inter-rater reliability among all raters, suggesting the AISP performs similarly at detecting the specified pathologies compared to human evaluators. Sensitivity and specificity for the AISP were assessed using human evaluators as the reference standard. The results revealed a trend of low sensitivity and high specificity, suggesting the AISP may produce a high rate of false negatives and may not be a good tool for initial screening. However, the low rate of false positives produced by the AISP suggests it may be beneficial as a "second set of eyes" because if it detects the specific pathology, there is a high likelihood that the pathology is present. With an understanding of the AISP, as an aid and not a substitute for veterinarians, the technology may increase dental radiography utilization and diagnostic potential.

Cluster Size Intelligence Prediction System for Young Women’s Clothing Using 3D Body Scan Data

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

Human-centric artificial intelligence

The essay explores the influence of artificial intelligence (AI) on society and its potential to take over jobs from humans. With the ongoing acceleration of technology and the increasing independence of machines, a reduced number of workers will be required. The significant progress of artificial intelligence indicates that numerous jobs such as those of paralegals, journalists, office workers, and even computer programmers are at the brink of becoming obsolete as robots and intelligent software are set to replace them. It examines the possibility of augmented intelligence and concentrates on machine learning and deep learning as possible approaches. The study indicates variables that determine how likely an occupation is to be automated and highlights the advantages of using AI to boost work productivity. The application of AI and the concerned problem associated with it has a huge impact on human society. Machine learning and deep learning are implemented to discuss the feasibility of augmented intelligence. Many scientific approaches suggest the factors that determine the automation potential of an occupation and the benefits of using AI to improve work efficiency. Data analysis and result comparison are used in the essay. The essay draws the conclusion that Artificial Intelligence should improve human productivity and propel the development of society, but not replace it.

Abstract WP65: Impact of Artificial Intelligence on Acute Ischemic Stroke Treatment in a Large Academic Healthcare System

Introduction: We aimed to evaluate the impact of the implementation of a stroke triage artificial intelligence (AI) software on a large academic healthcare system. Methods: A retrospective study was conducted in our spoke and hub network comparing equal corresponding periods of pre- and post- Viz.ai implementation between January 2021 and December 2022. Door to needle, door to puncture (DTP), successful reperfusion rates, transfer rates, and clinical outcomes at discharge were compared. Results: The analysis encompassed a total of 5,456 patients including 2313 pre- and 2275 post-Viz.ai patients across 11 spokes and 416 pre- and 452 post-Viz.ai patients across 4 hubs. Among 868 patients undergoing EVT at the 4 hubs, there was no differences in baseline data except for a higher baseline NIHSS score in the post Viz.ai group. We found an overall absolute reduction of transfers by 60 cases equating to a statistically significant decrease of 2.3% (P=0.04). The results showed an overall decrease by 13.5 minutes (8.1%) which was not statistically significant (P=0.3). There was an overall statistically significant decrease of 6 minutes (9.5%) in DTP times from 63 (23-98) minutes to 57 (23-79) minutes (P=0.01). No other differences in outcomes were noted between both groups. Conclusion: The automatic AI software was able to show a notable impact in our hub and spoke mature network driven by decrease of unnecessary transfers, DIDO and DTP. These data support the role of AI software as an effective triaging tool in the stroke and hub networks. More studies are needed to confirm these results.

Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.

This manuscript presents a full validation of MetaSystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module using a probability threshold of 96% including accuracy, precision studies, and evaluation of limit of AFB detection on respiratory samples when the technology is used with assistance. This study is complementary to the conversation started by Tomasello et al. on the use of image analysis artificial intelligence software in routine mycobacterial diagnostic activities within the context of high-throughput laboratories with low incidence of tuberculosis.

Utility of artificial intelligence for detection of pneumothorax on chest radiopgraphs done after transthoracic percutaneous transthoracic biopsy guided by computed tomography

ObjetiveTo assess the ability of an artificial intelligence software to detect pneumothorax in chest radiographs done after percutaneous transthoracic biopsy. Material and methodsWe included retrospectively in our study adult patients who underwent CT-guided percutaneous transthoracic biopsies from lung, pleural or mediastinal lesions from June 2019 to June 2020, and who had a follow-up chest radiograph after the procedure. These chest radiographs were read to search the presence of pneumothorax independently by an expert thoracic radiologist and a radiodiagnosis resident, whose unified lecture was defined as the gold standard, and the result of each radiograph after interpretation by the artificial intelligence software was documented for posterior comparison with the gold standard. ResultsA total of 284 chest radiographs were included in the study and the incidence of pneumothorax was 14.4%. There were no discrepancies between the two readers’ interpretation of any of the postbiopsy chest radiographs. The artificial intelligence software was able to detect 41/41 of the present pneumothorax, implying a sensitivity of 100% and a negative predictive value of 100%, with a specificity of 79.4% and a positive predictive value of 45%. The accuracy was 82.4%, indicating that there is a high probability that an individual will be adequately classified by the software. It has also been documented that the presence of Port-a-cath is the cause of 8 of the 50 of false positives by the software. ConclusionsThe software has detected 100% of cases of pneumothorax in the postbiopsy chest radiographs. A potential use of this software could be as a prioritisation tool, allowing radiologists not to read immediately (or even not to read) chest radiographs classified as non-pathological by the software, with the confidence that there are no pathological cases.

Development of intelligent system of global bibliographic search

The paper presents scientifically grounded principles of formation and algorithms of a global bibliographic retrieval system based on modern intelligent technologies (intelligent linguistic analysis of texts and database management) and Internet search methods. Within the framework of theoretical bibliography, for the first time taking into account the specifics of modern intelligent software, a new algorithm of an integrated automated system for global bibliographic retrieval as well as search for specialized publications to publish research papers was developed. The developed methods include the formation of a normalized database of keywords based on the Universal Decimal Classification (UDC) categories and the automatic assignment of keywords from this normalized database to research papers by an intelligent system for linguistic analysis of texts. The proposed use of keywords as data labels in the form of UDC categories, covering all branches of knowledge, makes it possible to precisely label and describe research papers and scientific publications (journals, proceedings of scientific conferences) to the fullest extent possible. The search for research papers by the proposed bibliographic retrieval system as well as the search for specialized publications to publish those research papers is carried out automatically by normalized keywords assigned to research papers and scientific journals based on intelligent linguistic analysis of the content of those research papers. The study makes a social contribution and shows how it is important to create and utilize best practices, processes and strategies of bibliographic retrieval, which are essential in the development of science in modern society. Besides, in social terms, the search for bibliographic information is one of the methods to solve the problem of finding a relation between personal and public knowledge. The implementation of the study results in accordance with the presented algorithms consists in designing a special global public Internet search resource.

The Knowledge of Students at Bursa Faculty of Medicine towards Artificial Intelligence: A Survey Study

Objectives: Artificial intelligence (AI) is being increasingly widely employed in medicine. Various artificial intelligence software solutions will most certainly be available to medical students when they begin their jobs after completing school. The goal of this study was to assess medical faculty students' knowledge and attitudes about AI in medicine.&#x0D; Methods: In 2020-2021, students at the University of Health Sciences, Bursa Training and Research Hospital participated in an online survey using Google Forms. The survey included sections assessing demographic features and attitudes toward AI in medicine.&#x0D; Results: The questionnaire was completed by 85 students (57 females and 28 males). 50 medical school students and 35 pediatric medical specialty students completed the questionnaire. The mean time spent on the internet per day was 7.5400 ±3.67123 hours in MS and 3.1143 ± 2.0547 hours in MA (p &lt; 0.001). All of the participants had heard AI before (p = 0.500). 62% of MS and 65.7% of MA worried about AI doing many things today; no statistically significant difference was found between the two groups (p = 0.453). 56.5% of participants felt “curiosity”, 16.4% felt “excited”, 11.8% felt “look positive”, 15.3% felt “frightened” about the increased use of artificial intelligence in our lives. 24% of the MS and 37.1% of the MA knew AI applications used in medicine; there was no statistically significant difference between the two groups (p = 0.191). The most commonly known applications of AI in medicine were in the fields of robotic surgery (10.5%) and radiology (3.6%). While 8% of the MS participated in the project / study related to AI; none of the MA participated, there was a statistically significant difference between the two groups (p = 0.036). On the contrary, the use of AI in medicine was 5.7% in MA; it was never possible in MS, there was no statistically significant difference between the two groups (p = 0.057). 91.8% of the participants would like to use AI in their future careers as physicians. 68% of MS and 94.3% of MA thought AI could not replace doctors in the future; there was a statistically significant difference between the two groups (p = 0.004).&#x0D; Conclusion: Medical students, who will be the physicians of the future, regarded AI applications positively, and they wanted to gain education and experience in this subject with deep curiosity. Since the knowledge level of future physicians in this field is not sufficient, it is important to provide more AI training in medicine, to participate in more projects in this field, and to increase the use of AI by medical students in the field of medicine.

Knowledge Base of Algorithmic and Software Base for Intellectualization of Underground Water Supply Facilities

This article examines the importance of the knowledge base in the development of an intelligent algorithmic and software complex of underground water intake facilities. When developing the computerized knowledge base of underground water intake facilities, the management knowledge base, the classification of knowledge bases, the use of conventional knowledge bases for creating expert systems and storing information about the facility will be studied. A knowledge base structure will be developed when creating an intelligent algorithmic and software complex of underground water intake facilities. When organizing the process of forming underground water intake facilities, the development of a knowledge base for the watertight properties of single-layer moisture layers will be studied. During the formation of underground water intake facilities, knowledge is calculated to solve the issues of determining the watertightness of single-layer earthworks.

Study on the Whole Process Supervision Technology of Digital Intelligent Enabling for Building Safety and Quality Appraisal Institutions

In order to fully implement the "Notice of The General Office of the State Council on Printing and Printing the Special Rectification Work Plan for the Safety of Self-built Houses", in view of the endless collapse of houses such as self-built houses, the traditional supervision methods of housing safety appraisal agencies are dishonest and the agency appraisal reports are distorted. Based on the principle of "the whole process of Internet + digital intelligence", this paper develops a set of digital and intelligent regulatory software system suitable for regulatory departments. It mainly introduces the basic application functions of this system, and takes the whole-area appraisal project of Taizhou City as an example to show that this regulatory system manages in all aspects such as human, machine, material, law and environment. The system has the advantages of thoughtful use steps, simple steps, scientific and accurate. At the end of this paper, the benefits and promotion value of the whole process of intelligent enabling supervision system are summarized and analyzed.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PurposeThe assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.MethodsIn three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.ResultsA total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).ConclusionThe quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

AI and modern experimental biology: A historical perspective

AI and modern experimental biology: A historical perspective Ute Deichmann, Director of the Jacques Loeb Centre for the History and Philosophy of the Life Sciences at Ben-Gurion University of the Negev, discusses the adoption and limitations of Artificial Intelligence within modern experimental biology. ‘How generative AI could disrupt scientific publishing.’ ‘ChatGPT use shows that the grant-application system is broken.’ ‘Companies say the technology will lead to faster drug development. Independent verification and clinical trials will determine whether the claim holds up.’ ‘AlphaFold touted as next big thing for drug discovery — but is it?’ These recent headlines from the journal Nature not only indicate the enormous impact of artificial intelligence (AI), the intelligence of machines or software, on research across various scientific disciplines but also point to challenges regarding AI reliability. What does intelligence here mean? The Cambridge Dictionary defines intelligence as ‘the ability to learn, understand, and make judgments or have opinions based on reason.’

Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach

ABSTRACT The aim of this study was to develop an innovative deep learning based intelligent software (DamageNet) and its mobile applications to classify seismic damage of Reinforced Concrete (RC) elements. Images of 2455 damaged elements that have been exposed to different destructive earthquakes were collected from the “datacenterhub” database. The DamageNet algorithm has been compared with the pretrained convolutional neural networks (CNN) algorithms (VGG16, ResNet-50, MobileNetV2 and EfficientNet) according to performance metrics. With the other models, a maximum test success of 89% was achieved, while with DamageNet a test success of 92% was achieved in damage classification. The mobile application developed based on the DamageNet model was tested in the field after the earthquakes (Mw:7.7 and Mw:7.6) in Kahramanmaraş/Turkey and classification success of 88% was obtained.

Improvement of the nutritional support management system for patients in intensive care units.

Nutritional support for patients hospitalized in the intensive care unit (ICU) is an important part of clinical treatment and care, but there are significant implementation difficulties. To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling. The division of functions, personnel training, system construction, development of an intelligent decision-making software system, quality control, and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients. Following the implementation of the whole process management system, the scores of ICU medical staff's knowledge, attitudes/beliefs, and practices regarding nutritional support were comprehensively enhanced. The proportion of hospital bed-days of total enteral nutrition (EN) in ICU patients increased from 5.58% to 11.46%, and the proportion of EN plus parenteral nutrition increased from 42.71% to 47.07%. The rate of EN initiation within 48 h of ICU admission increased from 37.50% to 48.28%, and the EN compliance rate within 72 h elevated from 20.59% to 31.72%. After the implementation of the project, the Self-rating Anxiety Scale score decreased from 61.07 ± 9.91 points to 52.03 ± 9.02 points, the Self-rating Depression Scale score reduced from 62.47 ± 10.50 points to 56.34 ± 9.83 points, and the ICU stay decreased from 5.76 ± 2.77 d to 5.10 ± 2.12 d. The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.