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Fulltext Path finding of static indoor mobile robot via AOR iterative method using harmonic potentials

A'qilah Ahmad Dahalan, Azali Saudi, Jumat Sulaiman

ZULFAQAR International Journal of Defence Science, Engineering & Technology, 2020;3(2):18-24.
MyJurnal

Mobile robots often have to discover a path of collision-free towards a specific goal point in their environment. We are trying to resolve the mobile robot problem iteratively by means of numerical technique. It is built on a method of potential field that count on the use of Laplace’s equation in the mobile robot’s configuration space to constrain/which reduces the generation of a potential function over regions. This paper proposed an iterative approach in solving robot path finding problem known as Accelerated Over-Relaxation (AOR). The experiment shows that these suggested approach can establish a smooth path between the starting and goal points by engaging with a finite-difference technique. The simulation results also show that a more rapidly solution with smoother path than the previous work is achieved via this numerical approach.

Matched MeSH terms: Artificial Intelligence
Fulltext Critical Device Reliability Assessment in Healthcare Services

Abd Rahman NH, Ibrahim AK, Hasikin K, Abd Razak NA

J Healthc Eng, 2023;2023:3136511.
PMID: 36860328 DOI: 10.1155/2023/3136511

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

Matched MeSH terms: Artificial Intelligence*
Fulltext A multinational study on the factors influencing university students' attitudes and usage of ChatGPT

Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al.

Sci Rep, 2024 Jan 23;14(1):1983.
PMID: 38263214 DOI: 10.1038/s41598-024-52549-8

Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.

Matched MeSH terms: Artificial Intelligence*
Fulltext A modified active appearance model based on an adaptive artificial bee colony

Abdulameer MH, Sheikh Abdullah SN, Othman ZA

ScientificWorldJournal, 2014;2014:879031.
PMID: 25165748 DOI: 10.1155/2014/879031

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Matched MeSH terms: Artificial Intelligence*
Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images

Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V

J Med Syst, 2018 Feb 17;42(4):58.
PMID: 29455440 DOI: 10.1007/s10916-018-0912-y

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Matched MeSH terms: Artificial Intelligence
A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

Abidi SS, Cheah YN, Curran J

IEEE Trans Inf Technol Biomed, 2005 Jun;9(2):193-204.
PMID: 16138536

Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism--i.e., clinical scenarios--that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.

Matched MeSH terms: Artificial Intelligence*
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
Transforming XML-based electronic patient records for use in medical case based reasoning systems

Abidi SS, Manickam S

Stud Health Technol Inform, 2000;77:709-13.
PMID: 11187645

Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.

Matched MeSH terms: Artificial Intelligence*
TIDE: an intelligent home-based healthcare information & diagnostic environment

Abidi SS

Stud Health Technol Inform, 1999;68:720-5.
PMID: 10724989

The 21st century promises to usher in an era of Internet based healthcare services--Tele-Healthcare. Such services augur well with the on-going paradigm shift in healthcare delivery patterns, i.e. patient centred services as opposed to provider centred services and wellness maintenance as opposed to illness management. This paper presents a Tele-Healthcare info-structure TIDE--an 'intelligent' wellness-oriented healthcare delivery environment. TIDE incorporates two WWW-based healthcare systems: (1) AIMS (Automated Health Monitoring System) for wellness maintenance and (2) IDEAS (Illness Diagnostic & Advisory System) for illness management. Our proposal comes from an attempt to rethink the sources of possible leverage in improving healthcare; vis-à-vis the provision of a continuum of personalised home-based healthcare services that emphasise the role of the individual in self health maintenance.

Matched MeSH terms: Artificial Intelligence*
Applying data mining in healthcare: an info-structure for delivering 'data-driven' strategic services

Abidi SS

Stud Health Technol Inform, 1999;68:453-6.
PMID: 10724926

Presently, there is a growing demand from the healthcare community to leverage upon and transform the vast quantities of healthcare data into value-added, 'decision-quality' knowledge, vis-à-vis, strategic knowledge services oriented towards healthcare management and planning. To meet this end, we present a Strategic Knowledge Services Info-structure that leverages on existing healthcare knowledge/data bases to derive decision-quality knowledge-knowledge that is extracted from healthcare data through services akin to knowledge discovery in databases and data mining.

Matched MeSH terms: Artificial Intelligence*
Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification

AbuHassan KJ, Bakhori NM, Kusnin N, Azmi UZM, Tania MH, Evans BA, et al.

Annu Int Conf IEEE Eng Med Biol Soc, 2017 Jul;2017:4512-4515.
PMID: 29060900 DOI: 10.1109/EMBC.2017.8037859

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.

Matched MeSH terms: Artificial Intelligence
Fulltext Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh RA, Nilashi M, Yousoof Ismail M, Alhargan A, Alghamdi A, Alzahrani AO, et al.

J Infect Public Health, 2022 Jan;15(1):75-93.
PMID: 34836799 DOI: 10.1016/j.jiph.2021.11.013

COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.

Matched MeSH terms: Artificial Intelligence
Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Abunama T, Othman F, Ansari M, El-Shafie A

Environ Sci Pollut Res Int, 2019 Feb;26(4):3368-3381.
PMID: 30511225 DOI: 10.1007/s11356-018-3749-5

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Matched MeSH terms: Artificial Intelligence*
Theoretically-derived molecular descriptors important in human intestinal absorption

Agatonovic-Kustrin S, Beresford R, Yusof AP

J Pharm Biomed Anal, 2001 May;25(2):227-37.
PMID: 11275432

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Matched MeSH terms: Artificial Intelligence
Fulltext ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol

Aggarwal A, Court LE, Hoskin P, Jacques I, Kroiss M, Laskar S, et al.

BMJ Open, 2023 Dec 07;13(12):e077253.
PMID: 38149419 DOI: 10.1136/bmjopen-2023-077253

INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy.
METHODS: ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs.
ETHICS AND DISSEMINATION: The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.

Matched MeSH terms: Artificial Intelligence*
EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms

Ahirwal MK, Kumar A, Singh GK

IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
PMID: 24407307 DOI: 10.1109/TCBB.2013.119

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Matched MeSH terms: Artificial Intelligence
Fulltext Optimization of power utilization in multimobile robot foraging behavior inspired by honeybees system

Ahmad FA, Ramli AR, Samsudin K, Hashim SJ

ScientificWorldJournal, 2014;2014:153162.
PMID: 24949491 DOI: 10.1155/2014/153162

Deploying large numbers of mobile robots which can interact with each other produces swarm intelligent behavior. However, mobile robots are normally running with finite energy resource, supplied from finite battery. The limitation of energy resource required human intervention for recharging the batteries. The sharing information among the mobile robots would be one of the potentials to overcome the limitation on previously recharging system. A new approach is proposed based on integrated intelligent system inspired by foraging of honeybees applied to multimobile robot scenario. This integrated approach caters for both working and foraging stages for known/unknown power station locations. Swarm mobile robot inspired by honeybee is simulated to explore and identify the power station for battery recharging. The mobile robots will share the location information of the power station with each other. The result showed that mobile robots consume less energy and less time when they are cooperating with each other for foraging process. The optimizing of foraging behavior would result in the mobile robots spending more time to do real work.

Matched MeSH terms: Artificial Intelligence
Fulltext Data on the mechanisms of antidiarrhoeal activity of methanol leaf extract of Combretum hypopilinum Diels (Combretaceae): Involvement of opioidergic and (α1 and β)-adrenergic pathways

Ahmad MH, Zezi AU, Anafi SB, Alhassan Z, Mohammed M, Danraka RN

Data Brief, 2021 Jun;36:107155.
PMID: 34041327 DOI: 10.1016/j.dib.2021.107155

This article describes the dataset for the elucidation of the possible mechanisms of antidiarrhoeal actions of methanol leaves extract of Combretum hypopilinum (Diels) Combretaceae in mice. The plant has been used in traditional medicine to treat diarrhoea in Nigeria and other African countries. We introduce the data for the antidiarrhoeal activity of the methanol leaf extract of Combretum hypopilinum at 1,000 mg/kg investigated using charcoal meal test in mice with loperamide (5 mg/kg) as the standard antidiarrhoeal agent. To elucidate the possible mechanisms of its antidiarrhoeal action, naloxone (2 mg/kg), prazosin (1 mg/kg), yohimbine (2 mg/kg), propranolol (1 mg/kg), pilocarpine (1 mg/kg) and isosorbide dinitrate (150 mg/kg) were separately administered to different groups of mice 30 minutes before administration of the extract. Each mouse was dissected using dissecting set, and the small intestine was immediately removed from pylorus to caecum, placed lengthwise on moist filter paper and measured the distance travelled by charcoal relative to the length of the intestine using a calibrated ruler in centimetre. Besides, the peristaltic index and inhibition of charcoal movement of each animal were calculated and recorded. The methods for the data collection is similar to the one used to investigate the possible pathways involved in the antidiarrhoeal action of Combretum hypopilinum in mice in the research article by Ahmad et al. (2020) "Mechanisms of Antidiarrhoeal Activity of Methanol Leaf Extract of Combretum hypopilinum Diels (Combretaceae): Involvement of Opioidergic and (α1 and β)-Adrenergic Pathways" (https://doi.org/10.1016/j.jep.2020.113750) [1]. Therefore, this datasets could form a basis for in-depth research to elucidate further the pharmacological properties of the plant Combretum hypopilinum and its bioactive compounds to develop standardized herbal product and novel compound for management of diarrhoea. It could also be instrumental for evaluating the plant's pharmacological potentials using other computational-based and artificial intelligence approaches, including predictive modelling and simulation.

Matched MeSH terms: Artificial Intelligence
Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.

Biomed Res Int, 2021;2021:9751564.
PMID: 34258283 DOI: 10.1155/2021/9751564

Objective: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Matched MeSH terms: Artificial Intelligence/trends*
Resource recovery from hydroponic wastewaters using microalgae-based biorefineries: A circular bioeconomy perspective

Ajeng AA, Rosli NSM, Abdullah R, Yaacob JS, Qi NC, Loke SP

J Biotechnol, 2022 Dec 10;360:11-22.
PMID: 36272573 DOI: 10.1016/j.jbiotec.2022.10.011

As the world's population grows, it is necessary to rethink how countries throughout the world produce food in order to replace the conventional and unsustainable agricultural techniques. Microalgae cultivation using a nutrient-rich solution from hydroponic systems not only presents a novel approach to solving problems pertaining to the impact of the discharges on the natural environment but also provides a plethora of other biotechnological applications particularly in the productions of high value-added products and plants growth stimulants, which can be potentially assimilated into the circular bioeconomy (CBE) in the hydroponic sector. In this review, the potential and practicability of microalgae to be merged into hydroponics CBE are reviewed. Overall, the integration of microalgal biorefineries in hydroponics systems can be realized after considering their Technology Readiness Level and System Readiness Level beforehand. Several suggestions on strains and hydroponics system improvement using existing biotechnological tools, Artificial Intelligence (AI) and nanobiotechnology in support of the CBE will be covered.

Matched MeSH terms: Artificial Intelligence

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