Displaying publications 201 - 220 of 325 in total

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  1. Integrated energy informatics technology on microalgae-based wastewater treatment to bioenergy production: A review
    Yu KL, Ong HC, Zaman HB
    J Environ Manage, 2024 Sep;368:122085.
    PMID: 39142099 DOI: 10.1016/j.jenvman.2024.122085
    The production of renewable biofuel through microalgae and green technology can be a promising solution to meet future energy demands whilst reducing greenhouse gases (GHG) emissions and recovering energy for a carbon-neutral bio-economy and environmental sustainability. Recently, the integration of Energy Informatics (EI) technology as an emerging approach has ensured the feasibility and enhancement of microalgal biotechnology and bioenergy applications. Integrating EI technology such as artificial intelligence (AI), predictive modelling systems and life cycle analysis (LCA) in microalgae field applications can improve cost, efficiency, productivity and sustainability. With the approach of EI technology, data-driven insights and decision-making, resource optimization and a better understanding of the environmental impact of microalgae cultivation could be achieved, making it a crucial step in advancing this field and its applications. This review presents the conventional technologies in the microalgae-based system for wastewater treatment and bioenergy production. Furthermore, the recent integration of EI in microalgal technology from the AI application to the modelling and optimization using predictive control systems has been discussed. The LCA and techno-economic assessment (TEA) in the environmental sustainability and economic point of view are also presented. Future challenges and perspectives in the microalgae-based wastewater treatment to bioenergy production integrated with the EI approach, are also discussed in relation to the development of microalgae as the future energy source.
    Matched MeSH terms: Artificial Intelligence
  2. Fulltext Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment
    Ramadan MNA, Ali MAH, Khoo SY, Alkhedher M, Alherbawi M
    Ecotoxicol Environ Saf, 2024 Sep 15;283:116856.
    PMID: 39151373 DOI: 10.1016/j.ecoenv.2024.116856
    Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.
    Matched MeSH terms: Artificial Intelligence
  3. Fulltext Current Status and Future Trends in Removal, Control, and Mitigation of Algae Food Safety Risks for Human Consumption
    Wu G, Zhuang D, Chew KW, Ling TC, Khoo KS, Van Quyen D, et al.
    Molecules, 2022 Oct 06;27(19).
    PMID: 36235173 DOI: 10.3390/molecules27196633
    With the rapid development of the economy and productivity, an increasing number of citizens are not only concerned about the nutritional value of algae as a potential new food resource but are also, in particular, paying more attention to the safety of its consumption. Many studies and reports pointed out that analyzing and solving seaweed food safety issues requires holistic and systematic consideration. The three main factors that have been found to affect the food safety of algal are physical, chemical, and microbiological hazards. At the same time, although food safety awareness among food producers and consumers has increased, foodborne diseases caused by algal food safety incidents occur frequently. It threatens the health and lives of consumers and may cause irreversible harm if treatment is not done promptly. A series of studies have also proved the idea that microbial contamination of algae is the main cause of this problem. Therefore, the rapid and efficient detection of toxic and pathogenic microbial contamination in algal products is an urgent issue that needs to be addressed. At the same time, two other factors, such as physical and chemical hazards, cannot be ignored. Nowadays, the detection techniques are mainly focused on three major hazards in traditional methods. However, especially for food microorganisms, the use of traditional microbiological control techniques is time-consuming and has limitations in terms of accuracy. In recent years, these two evaluations of microbial foodborne pathogens monitoring in the farm-to-table chain have shown more importance, especially during the COVID-19 pandemic. Meanwhile, there are also many new developments in the monitoring of heavy metals, algal toxins, and other pollutants. In the future, algal food safety risk assessment will not only focus on convenient, rapid, low-cost and high-accuracy detection but also be connected with some novel technologies, such as the Internet of Things (artificial intelligence, machine learning), biosensor, and molecular biology, to reach the purpose of simultaneous detection.
    Matched MeSH terms: Artificial Intelligence
  4. Construction 4.0 technology evaluation using fuzzy TOPSIS: comparison between sustainability and resiliency, well-being, productivity, safety, and integrity
    Shafei H, Rahman RA, Lee YS
    Environ Sci Pollut Res Int, 2024 Feb;31(10):14858-14893.
    PMID: 38285259 DOI: 10.1007/s11356-024-31862-9
    This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.
    Matched MeSH terms: Artificial Intelligence
  5. Fulltext A smart wheelchair prototype based on hand gesture control
    Shaari AR, Mohd Jani MN, Mohamed Yunus AS
    Journal of Engineering and Science Research, 2017;1(2):209-214.
    MyJurnal
    Wheelchair has been an important assistive device and the demand are ever rising because of the increasing physically handicapped and old age populations. The recent development in the robotics artificial intelligence extends vast scope for developing the more advanced and intelligent one to overcome limitations of the existing traditional wheelchairs. The prototype smart wheelchair were present on this paper using hardware implementation with the help of simple hand gesture which is comprises of an accelerometer mounted on the hand glove senses the tilt angle of the user hand movements and transmits control signal to the receiver mounted on wheelchair. This will interpret the movement accordingly required by user. The wheelchair control unit is developed by integration of ATMEGA328 microcontroller with Arduino UNO. The wheelchair is developed to allow peoples to move safely and put reliability in accomplishment of some important tasks in daily life.
    Matched MeSH terms: Artificial Intelligence
  6. Fulltext The potential of blockchain technology for occupational safety and health management system
    Rafizah Musa, Mohamad Syazli Fathib
    Journal of Occupational Safety and Health, 2019;16(1):27-31.
    MyJurnal
    Industries in Malaysia are entering a period of major disruption caused by new technologies such as Artificial Intelligent, Robotics, Blockchain, Nanotechnology as well as Building Information Modelling (BIM) and the Internet of Things (IoT). In this fourth industrial revolution where information is generated and exchanged at a rapid and huge scale, its reliability is of paramount importance. The success of Occupational Safety & Health Management System (OSHMS) is highly dependent on the reliability of the information gathered and used, where a large number of intermediaries authenticate the information to establish trust between the stakeholders. Blockchain technology is able to do verification by virtue of secured distributed storage brings about a paradigm shift in the way we establish trust. This paper gives an overview of the potential use of Blockchain technology for Occupational Safety & Health Management System. The discussions focused on the benefits and challenges of implementing the Blockchain technology in OSHMS. The conclusion is drawn based on the strength in the characteristics provided by the Blockchain technology itself.
    Matched MeSH terms: Artificial Intelligence
  7. Fulltext Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression
    Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM
    Sensors (Basel), 2016 Jul 19;16(7).
    PMID: 27447638 DOI: 10.3390/s16071115
    The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
    Matched MeSH terms: Artificial Intelligence
  8. Fulltext Classification of Partial Discharge Measured under Different Levels of Noise Contamination
    Jee Keen Raymond W, Illias HA, Abu Bakar AH
    PLoS One, 2017;12(1):e0170111.
    PMID: 28085953 DOI: 10.1371/journal.pone.0170111
    Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.
    Matched MeSH terms: Artificial Intelligence
  9. An intelligent tele-healthcare environment offering person-centric and wellness-maintenance services
    Abidi SS
    J Med Syst, 2001 Jun;25(3):147-65.
    PMID: 11433545
    Worldwide healthcare delivery trends are undergoing a subtle paradigm shift--patient centered services as opposed to provider centered services and wellness maintenance as opposed to illness management. In this paper we present a Tele-Healthcare project TIDE--Tele-Healthcare Information and Diagnostic Environment. TIDE manifests an 'intelligent' healthcare environment that aims to ensure lifelong coverage of person-specific health maintenance decision-support services--i.e., both wellness maintenance and illness management services--ubiquitously available via the Internet/WWW. Taking on an all-encompassing health maintenance role--spanning from wellness to illness issues--the functionality of TIDE involves the generation and delivery of (a) Personalized, Pro-active, Persistent, Perpetual, and Present wellness maintenance services, and (b) remote diagnostic services for managing noncritical illnesses. Technically, TIDE is an amalgamation of diverse computer technologies--Artificial Intelligence, Internet, Multimedia, Databases, and Medical Informatics--to implement a sophisticated healthcare delivery infostructure.
    Matched MeSH terms: Artificial Intelligence
  10. Fulltext ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display
    Tanwar G, Chauhan R, Yafi E
    Sensors (Basel), 2021 Feb 22;21(4).
    PMID: 33671822 DOI: 10.3390/s21041527
    We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
    Matched MeSH terms: Artificial Intelligence
  11. Fulltext Implementing uberization in malaysian healthcare services
    Rajesh Kumar Muniandy, Merly Grace Lansing
    Borneo Journal of Medical Sciences, 2020;14(2):53-57.
    MyJurnal
    Getting appropriate healthcare is a challenge to the citizens in Malaysia due to the limited facilities, healthcare providers, and cost of healthcare. Uberization of healthcare will help fill this gap. Uberization helps modify the market or economic model with the introduction of a cheaper and more effective alternative service by introducing a different way of buying or using it, with the use of mobile technology. With powerful artificial intelligence engines operating on cloud servers, mobile apps can provide a better healthcare experience for patients. With uberization application, the patient need not come to the hospital to see a doctor before a treatment can be planned. Once a request is made by the patient, the healthcare providers can come to see the patient at an agreed place. This article aims to explore the possible uberization of healthcare in Malaysia.
    Matched MeSH terms: Artificial Intelligence
  12. Biosensing human blood clotting factor by dual probes: Evaluation by deep long short-term memory networks in time series forecasting
    Gopinath SCB, Ismail ZH, Shapiai MI, Sobran NMM
    Biotechnol Appl Biochem, 2022 Jun;69(3):930-938.
    PMID: 33835514 DOI: 10.1002/bab.2164
    Artificial intelligence of things (AIoT) has become a potential tool for use in a wide range of fields, and its use is expanding in interdisciplinary sciences. On the other hand, in a clinical scenario, human blood-clotting disease (Royal disease) detection has been considered an urgent issue that has to be solved. This study uses AIoT with deep long short-term memory networks for biosensing application and analyzes the potent clinical target, human blood clotting factor IX, by its aptamer/antibody as the probe on the microscaled fingers and gaps of the interdigitated electrode. The earlier results by the current-volt measurements have shown the changes in the surface modification. The limit of detection (LOD) was noticed as 1 pM with the antibody as the probe, whereas the aptamer behaved better with the LOD at 100 fM. The time-series predictions from the AIoT application supported the obtained results with the laboratory analyses using both probes. This application clearly supports the results obtained from the interdigitated electrode sensor as aptamer to be the better option for analyzing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application and can be followed for other clinical biomarkers.
    Matched MeSH terms: Artificial Intelligence
  13. Fulltext IoT-based smart wheelchair system for physically impaired person
    Muhammad Afiq Mohd Aizam, Nor Shahanim Mohamad Hadis, Samihah Abdullah
    ESTEEM Academic Journal, 2020;16(1):59-73.
    MyJurnal
    Disabled persons usually require an assistant to help them in their daily routines especially for their mobility. The limitation of being physically impaired affects the quality of life in executing their daily routine especially the ones with a wheelchair. Pushing a wheelchair has its own side effects for the user especially the person with hands and arms impairments. This paper aims to develop a smart wheelchair system integrated with home automation. With the advent of the Internet of Things (IoT), a smart wheelchair can be operated using voice command through the Google assistant Software Development Kit (SDK). The smart wheelchair system and the home automation of this study were powered by Raspberry Pi 3 B+ and NodeMCU, respectively. Voice input commands were processed by the Google assistant Artificial Intelligence Yourself (AIY) to steer the movement of wheelchair. Users were able to speak to Google to discover any information from the website. For the safety of the user, a streaming camera was added on the wheelchair. An improvement to the wheelchair system that was added on the wheelchair is its combination with the home automation to help the impaired person to control their home appliances through Blynk application.
    Observations on three voice tones (low, medium and high) of voice command show that the minimum voice intensity for this smart wheelchair system is 68.2 dB. Besides, the user is also required to produce a clear voice command to increase the system accuracy.
    Matched MeSH terms: Artificial Intelligence
  14. Fulltext Paradigm shift: a need of the hour in the techno era
    Krishna Dilip Murthy
    Borneo Journal of Medical Sciences, 2020;14(1):1-2.
    MyJurnal
    It is time to cogitate as to “how and what ”we teach in the medical faculties/schools. We are aware that the generation of students is different; called the “Z”-generation. So, in keeping with the trends in the field of globalization, IR 4.0, artificial intelligence and the techno era, there is a need to change and become flexible to meet the demands of the artificial intelligence and the era. The future generations will be the Centennials who will adapt heutagogy (pronounced as: hyoo-tuh-goh-jee) principles to learn what they are passionate about. Heutagogy was first defined by Hase and Kenyon (2000) as a form of “self-determined learning”. So, in other words, pedagogy (the art and science of teaching children) and andragogy (the art and science of teaching adults) periods are almost over or take a back seat. In simpler terms, pedagogy is faculty-centred education; andragogy is student-centred education is not enough1. Heutagogy is self-directed, transformative and the present thing2. We need to be aware and cognizant of this fact in order to cater to our clients of the next generation.
    Matched MeSH terms: Artificial Intelligence
  15. Robotic Mounted Rail Arm System for implementing effective workplace safety for migrant workers
    Wei H, Rahman MA, Hu X, Zhang L, Guo L, Tao H, et al.
    Work, 2021;68(3):845-852.
    PMID: 33612527 DOI: 10.3233/WOR-203418
    BACKGROUND: The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items.

    OBJECTIVE: In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements.

    RESULTS: The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods.

    CONCLUSION: This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.

    Matched MeSH terms: Artificial Intelligence
  16. Advancement in biosensor: "Telediagnosis" and "remote digital imaging"
    Gopinath SCB, Ismail ZH, Shapiai MI, Yasin MNM
    Biotechnol Appl Biochem, 2021 May 19.
    PMID: 34009645 DOI: 10.1002/bab.2196
    Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
    Matched MeSH terms: Artificial Intelligence
  17. An insight into the role of Artificial Intelligence in the early diagnosis of Alzheimer's disease
    Verma RK, Pandey M, Chawla P, Choudhury H, Mayuren J, Bhattamisra SK, et al.
    CNS Neurol Disord Drug Targets, 2021 May 11.
    PMID: 33982657 DOI: 10.2174/1871527320666210512014505
    BACKGROUND: The complication of Alzheimer's disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the foremost hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated.

    METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.

    CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.

    Matched MeSH terms: Artificial Intelligence
  18. Fulltext Sagittal Jaw Relationship of Different Types of Cleft and Non-cleft Individuals
    Alam MK, Alfawzan AA, Haque S, Mok PL, Marya A, Venugopal A, et al.
    Front Pediatr, 2021;9:651951.
    PMID: 34026687 DOI: 10.3389/fped.2021.651951
    To investigate whether the craniofacial sagittal jaw relationship in patients with non-syndromic cleft differed from non-cleft (NC) individuals by artificial intelligence (A.I.)-driven lateral cephalometric (Late. Ceph.) analysis. The study group comprised 123 subjects with different types of clefts including 29 = BCLP (bilateral cleft lip and palate), 41 = UCLP (unilateral cleft lip and palate), 9 = UCLA (unilateral cleft lip and alveolus), 13 = UCL (unilateral cleft lip) and NC = 31. The mean age was 14.77 years. SNA, SNB, ANB angle and Wits appraisal was measured in lateral cephalogram using a new innovative A.I driven Webceph software. Two-way ANOVA and multiple-comparison statistics tests were applied to see the differences between gender and among different types of clefts vs. NC individuals. A significant decrease (p < 0.005) in SNA, ANB, Wits appraisal was observed in different types of clefts vs. NC individuals. SNB (p > 0.005) showed insignificant variables in relation to type of clefts. No significant difference was also found in terms of gender in relation to any type of clefts and NC group. The present study advocates a decrease in sagittal development (SNA, ANB and Wits appraisal) in different types of cleft compared to NC individuals.
    Matched MeSH terms: Artificial Intelligence
  19. Fulltext Three-dimensional semi-automated volumetric assessment of the pulp space of teeth following regenerative dental procedures
    Shetty H, Shetty S, Kakade A, Shetty A, Karobari MI, Pawar AM, et al.
    Sci Rep, 2021 11 09;11(1):21914.
    PMID: 34754049 DOI: 10.1038/s41598-021-01489-8
    The volumetric change that occurs in the pulp space over time represents a critical measure when it comes to determining the secondary outcomes of regenerative endodontic procedures (REPs). However, to date, only a few studies have investigated the accuracy of the available domain-specialized medical imaging tools with regard to three-dimensional (3D) volumetric assessment. This study sought to compare the accuracy of two different artificial intelligence-based medical imaging programs namely OsiriX MD (v 9.0, Pixmeo SARL, Bernex Switzerland, https://www.osirix-viewer.com ) and 3D Slicer ( http://www.slicer.org ), in terms of estimating the volume of the pulp space following a REP. An Invitro assessment was performed to check the reliability and sensitivity of the two medical imaging programs in use. For the subsequent clinical application, pre- and post-procedure cone beam computed tomography scans of 35 immature permanent teeth with necrotic pulp and periradicular pathosis that had been treated with a cell-homing concept-based REP were processed using the two biomedical DICOM software programs (OsiriX MD and 3D Slicer). The volumetric changes in the teeth's pulp spaces were assessed using semi-automated techniques in both programs. The data were statistically analyzed using t-tests and paired t-tests (P = 0.05). The pulp space volumes measured using both programs revealed a statistically significant decrease in the pulp space volume following the REP (P  0.05). The mean decreases in the pulp space volumes measured using OsiriX MD and 3D Slicer were 25.06% ± 19.45% and 26.10% ± 18.90%, respectively. The open-source software (3D Slicer) was found to be as accurate as the commercially available software with regard to the volumetric assessment of the post-REP pulp space. This study was the first to demonstrate the step-by-step application of 3D Slicer, a user-friendly and easily accessible open-source multiplatform software program for the segmentation and volume estimation of the pulp spaces of teeth treated with REPs.
    Matched MeSH terms: Artificial Intelligence
  20. Fulltext Tuberculosis disease diagnosis using artificial immune recognition system
    Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, et al.
    Int J Med Sci, 2014;11(5):508-14.
    PMID: 24688316 DOI: 10.7150/ijms.8249
    There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.
    Matched MeSH terms: Artificial Intelligence
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