Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement.

RATIONALE AND OBJECTIVES: Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. MATERIALS AND METHODS: A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. RESULTS: The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). CONCLUSION: The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion.

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

Comparison of computer controlled local anesthetic delivery and traditional injection regarding disruptive behaviour, pain, anxiety and biochemical parameters: a randomized controlled trial.

The study herein evaluated the effects of infiltrative anesthesia administered via different ways. Resultantly, the pain and anxiety were monitored using psychometric, physiological and biochemical methods. Sixty children aged 7-11 years (8.73 ± 1.38) were included in the study. They were divided into 2 groups (n = 30): Traditional injection (control group), and computer controlled local analgesic delivery (CCLAD) (study group). Pulse, oxygen saturation (SpO2), and salivary cortisol levels were recorded, and the scales data (Visual Analogue Scale (VAS), Wong-Baker Faces Rating Pain Scale (WBS), Modified Child Dental Anxiety Scale (MCDAS) and Face, Leg, Activity, Cry, Consolability Behavioral Pain Assessment Scale (FLACC)) were evaluated. The data were statistically analyzed. Age and gender had not much impact on the measured parameters (p > 0.05). SpO2 values in both groups were not significantly different (p > 0.05). Pulse, VAS, WBS, MCDAS, FLACC and salivary cortisol values were increased after the anesthesia in control group (p < 0.05). WBS, MCDAS, FLACC and salivary cortisol values were decreased after the anesthesia in study group compared to the control (p < 0.05). It was inferred that computer controlled local analgesic delivery system could be preferred in pediatric patients because of reduced pain and anxiety.

Preliminary study on molecular mechanism of COVID-19 intervention by Polygonum cuspidatum through computer bioinformatics.

To explore the mechanism of action of Polygonum cuspidatum in intervening in coronavirus disease 2019 using a network pharmacology approach and to preliminarily elucidate its mechanism. The active ingredients and action targets of P cuspidatum were classified and summarized using computer virtual technology and molecular informatics methods. The active ingredients and relevant target information of P cuspidatum were identified using the TCM Systematic Pharmacology Database and Analysis Platform, the TCM Integrated Pharmacology Research Platform v2.0, and the SwissTarget database. The GENECARDS database was used to search for COVID-19 targets. The STRING database was analyzed and combined with Cytoscape 3.7.1 software to construct a protein interaction network map to screen the core targets. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was then performed. The core compound, polydatin, was selected and the core targets were analyzed by computer virtual docking using software such as discovery studio autodock tool. In vitro cell models were constructed to experimentally validate the activity of the core compound, polydatin. By computer screening, we identified 9 active ingredients and their corresponding 286 targets from P cuspidatum. A search of the GENECARDS database for COVID-19 yielded 303 core targets. By mapping the active ingredient targets to the disease targets, 27 overlapping targets could be extracted as potential targets for the treatment of COVID-19 with P cuspidatum. In addition, the enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathway on core targets showed that the coronavirus disease, MAPK signaling pathway, NF kappa B signaling pathway, and other signaling pathways were highly enriched. Combined with the degree-high target analysis in the protein interaction network, it was found to be mainly concentrated in the NF-kappaB (NF-κB) signaling pathway, indicating that the NF-κB signaling pathway may be an important pathway for P cuspidatum intervention. In vitro assays showed no effect of 0.1 to 10 µM polydatin on cell viability, but an inhibitory effect on the transcriptional activity of NF-κB-RE. Molecular docking showed stable covalent bonding of polydatin molecules with Il-1ß protein at residue leu-26, TNF protein ser-60, residue gly-121, and residue ile-258 of ICAM-1 protein, indicating a stable docking result. The treatment of COVID-19 with P cuspidatum is characterized by multi-component, multi-target, and multi-pathway, which can exert a complex network of regulatory effects through the interaction between different targets, providing a new idea and basis for further exploration of the mechanism of action of P cuspidatum in the treatment of COVID-19.

[Combination of acupuncture and medication for dry eye complicated with computer vision syndrome: a randomized controlled trial].

OBJECTIVE: To compare the clinical efficacy of acupuncture, Chinese medication and combination of acupuncture and medication in the treatment of dry eye complicated with computer vision syndrome (CVS). METHODS: A total of 152 patients with dry eye complicated with CVS were randomly divided into an acupuncture-medication group (38 cases, 1 case was removed), an acupuncture group (38 cases, 1 case dropped off), a Chinese medication group (38 cases, 1 case was removed), and a western medication group (38 cases, 1 case dropped off). In the western medication group, sodium hyaluronate eye drop combined with esculin and digitalis glycosides eye drop were used. In the acupuncture group, acupuncture was applied at bilateral Taiyang (EX-HN 5)， Cuanzhu (BL 2), Fengchi (GB 20), Qimen (LR 14) , and Hegu (LI 4) etc., once a day. In the Chinese medication group, Yiqi Congming decoction formula ganule was given orally, one dose a day. In the acupuncture-medication group, acupuncture combined with Yiqi Congming decoction formula granule were used. All groups were treated for 14 d. The non-invasive first tear film break-up time (NIBUT f), non-invasive average tear film break-up time (NIBUT av), tear meniscus height (TMH), ocular surface disease index (OSDI) score, and CVS symptom score were compared between the patients of each group before and after treatment. RESULTS: After treatment, the NIBUT f, NIBUT av, and TMH were increased compared with those before treatment in the patients of the 4 groups (P<0.01); the NIBUT f and NIBUT av in the acupuncture-medication group and the acupuncture group were higher than those in the Chinese medication group and the western medication group (P<0.05), and the TMH in the acupuncture-medication group and the Chinese medication group were higher than those in the acupuncture group and the western medication group (P<0.05). After treatment, the OSDI scores, the various scores and total scores of CVS (except for head symptom score in the western medication group) were decreased compared with those before treatment in the patients of the 4 groups (P<0.01). The OSDI score, total score, eye symptom score, and body symptom score of CVS in the acupuncture-medication group were lower than those in the acupuncture group, the Chinese medication group, and the western medication group (P<0.01, P<0.05), the head symptom score of the acupuncture-medication group was lower than that in the western medication group (P<0.05), and the CVS physical symptom scores and mental cognitive symptom scores of the acupuncture-medication group and the acupuncture group were lower than those in the Chinese medication group and the western medication group (P<0.05). CONCLUSION: Acupuncture has advantages in improving NIBUT f, NIBUT av, and CVS physical symptoms and cognitive symptoms, and the Chinese medication has advantage in improving TMH. The combination of acupuncture and Chinese medication has better effects compared with monotherapy.

Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique.

Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treatment, and control of hypertension. In recent years, face video based non-contact blood pressure prediction is a promising research topic. Interestingly, face diagnosis has been an important part of traditional Chinese medicine (TCM) for thousands of years. TCM practitioners observe some typical regions of the face to determine the health status of the Zang Fu organs (i.e., heart). However, the effectiveness of face diagnosis theory in conjunction with computer vision analysis techniques to predict blood pressure is unclear. We proposed an artificial intelligence framework for predicting blood pressure using deep convolutional neural networks in this study. First, we extracted pulse wave signals through 652 facial videos. Then, we trained and compared nine artificial neural networks and chose the best performed prediction model, with an overall true predict rate of 90%. We also investigated the impact of face reflex regions selection on blood pressure prediction model, and the five face regions outperformed. Our high effectiveness and stability framework may provide an objective and convenient computer-aided blood pressure prediction method for hypertension screening and disease prevention.

Phytochemical Analysis, In Vitro Biological Activities, and Computer-Aided Analysis of Potentilla nepalensis Hook Compounds as Potential Melanoma Inhibitors Based on Molecular Docking, MD Simulations, and ADMET.

Potentilla nepalensis Hook is a perennial Himalayan medicinal herb of the Rosaceae family. The present study aimed to evaluate biological activities such as the antioxidant, antibacterial, and anticancer activities of roots and shoots of P. nepalensis and its synergistic antibacterial activity with antibacterial drugs. Folin-Ciocalteau and aluminium chloride methods were used for the calculation of total phenolic (TPC) and flavonoid content (TFC). A DPPH radical scavenging assay and broth dilution method were used for the determination of the antioxidant and antibacterial activity of the root and shoot extracts of P. nepalensis. Cytotoxic activity was determined using a colorimetric MTT assay. Further, phytochemical characterization of the root and shoot extracts was performed using the Gas chromatography-mass spectrophotometry (GC-MS) method. The TPC and TFC were found to be higher in the methanolic root extract of P. nepalensis. The methanolic shoot extract of P. nepalensis showed good antioxidant activity, while then-hexane root extract of P. nepalensis showed strong cytotoxic activity against tested SK-MEL-28 cells. Subsequently, in silico molecular docking studies of the identified bioactive compounds predicted potential anticancer properties. This study can lead to the production of new herbal medicines for various diseases employing P. nepalensis, leading to the creation of new medications.

Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction.

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

Construction of a virtual reality platform for computer-aided navigation Lingnan bone setting technique.

To establish a standard Traditional Chinese medicine (TCM) bone setting technique, standardize the operation and inherit the TCM bone setting technique. This project was based on the interactive tracking of bone setting techniques with a dedicated position tracker, the motion tracking of bone setting techniques based on RGBD (Red Green Blue Depth) cameras, the digital analysis of bone setting techniques, and the design of the virtual reality platform for bone setting techniques. These key technical researches were combined to construct an interactive bone setting technique. The virtual simulation system can reproduce the implementation process of the expert's bone setting technique. The user can observe the implementation of the manipulative technique from multiple angles; through human-computer interaction, the whole process of implementation of the bone setting technique can be simulated, and the movement and reduction of the affected bone can be observed at the same time. It can be used as a teaching and training system for assisting bone setting techniques. Students can use the system to carry out repeated self-training, and can instantly compare with the standard techniques of the expert database, breaking the traditional teaching mode of 'expected and unspeakable' and avoid directly using patients. Therefore, this research makes it possible to reduce teaching costs, reduce risks, improve teaching quality, and make up for the lack of teaching conditions. It is very positive for the inheritance of the traditional Chinese 'intangible culture' of bone setting techniques, and to promote the digitalization and standardization of bone setting techniques.

Using computer technology to digitally record bone-setting manipulations.Construct a virtual simulation system for interactive bone-setting manipulation.Promote the digitization and standardization of bone-setting techniques.

Computer-Assisted Levothyroxine Dose Selection for the Treatment of Postoperative Hypothyroidism.

Background: Thyroid hormone replacement with levothyroxine (LT4) is a recommended treatment for patients undergoing thyroidectomy. The starting LT4 dose is frequently calculated based on the patient's weight. However, the weight-based LT4 dosing performs poorly in clinical practice, with only ∼30% of patients achieving target thyrotropin (TSH) levels at the first thyroid function testing after treatment initiation. A better way to calculate the LT4 dose for patients with postoperative hypothyroidism is needed. Methods: In this retrospective cohort study we used demographic, clinical, and laboratory data for 951 patients after thyroidectomy and several regression and classification machine learning methods to develop an LT4 dose calculator for treating postoperative hypothyroidism targeting the desired TSH level. We compared the accuracy with the current standard-of-care practice and other published algorithms and evaluated generalizability with fivefold cross-validation and out-of-sample testing. Results: The retrospective clinical chart review showed that only 285/951 (30%) patients met their postoperative TSH goal. Obese patients were overtreated with LT4. An ordinary least squares regression based on weight, height, age, sex, calcium supplementation, and height:sex interaction predicted prescribed LT4 dose in 43.5% of all patients and 45.3% of patients with normal postoperative TSH (0.45-4.5 mIU/L). The ordinal logistic regression, artificial neural networks regression/classification, and random forest methods achieved comparable performance. LT4 calculator recommended lower LT4 doses to obese patients. Conclusions: The standard-of-care LT4 dosing does not achieve the target TSH in most thyroidectomy patients. Computer-assisted LT4 dose calculation performs better by considering multiple relevant patient characteristics and providing personalized and equitable care to patients with postoperative hypothyroidism. Prospective validation of LT4 calculator performance in patients with various TSH goals is needed.

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology.

In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants' self-evaluation. Affective computing uses biometrics to recognize people's emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform's effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human-robot interaction.

An innovative virtual reality training tool for the pre-hospital treatment of cranialmaxillofacial trauma.

Virtual reality (VR) surgery using the High Technology Computer Corporation Very Immersive Virtual Experience professional 2(HTC VIVE Pro2) suite is a multi-sensory, holistic surgical training experience. A multimedia combination including videos and three-dimensional interaction in VR has been developed to enable trainees to experience a realistic battlefield environment. The innovation allows trainees to interact with the individual components of the cranialmaxillofacial(CMF) anatomy and apply surgical instruments while watching close-up stereoscopic three-dimensional videos of the surgery. In this study, a novel training tool for the pre-hospital treatment of CMF trauma based on immersive virtual reality (iVR) was developed and validated. Twenty-five CMF surgeons evaluated the application for face and content validity. Using a structured assessment process, the surgeons commented on the content of the developed training tool, its realism and usability and the applicability of VR surgery for CMF trauma rescue simulation training. The results confirmed the applicability of VR for delivering training in the pre-hospital treatment of CMF trauma. Modifications were suggested to improve the user experience and interactions with the surgical instruments. This training tool is ready for testing with surgical trainees.

Comparison of the Degree of Staining of Computer-Aided Design-Computer-Aided Manufacture (CAD-CAM) Ceramic Veneers by Green Tea, Coffee, and Coca-Cola Using a Digital Spectrophotometer.

BACKGROUND The majority of dental professionals currently recognize lithium disilicate E-max ceramic veneers as a the most widely used, conservative, and effective cosmetic materials in dentistry. This study aimed to compare the degree of surface changes - roughness (Ra), depth (Rz), and mean color changes (ΔE00) - of computer-aided design-computer-aided manufactured (CAD/CAM) ceramic veneers materials of varying thicknesses caused by staining by green tea, coffee, and Coca-Cola using digital spectrophotometer. MATERIAL AND METHODS This study was conducted at King Khalid University, College of Dentistry. Lithium disilicate glass ceramic (LDGC) material was used to create 60 rectangular slices using the CAD/CAM system. The material thickness and the type of beverage were measured. The specimens were immersed in beverages according to the manufacturer's instructions. Specimen description and tomography were completed with a 3D noncontact surface metrology using interferometry. The "VITA Easy-Shade" spectrophotometer was used to measure ΔE00. It was recorded after 2 weeks for different material thicknesses after immersing samples in green tea, coffee, and Coca-Cola staining materials.f RESULTS Significant changes in ceramic thickness were found in Ra and Rz of 0.07 and 1.00 mm after 14 days of staining. Coca-Cola showed a significant difference in Ra and Rz with 1.00 mm thickness measurement compared to the 0.07 mm group with ≤ of 0.05, which was considered statistically significant. Highest ΔE00 were recorded among samples stained by Coca-Cola, followed by coffee, for both thicknesses. CONCLUSIONS Those findings support previous studies using spectrophotometric analysis of staining of CAD-CAM ceramic veneers that Coca-Cola followed by coffee resulted in the greatest color ΔE00 change.

Study on Computer Screening and Drug Properties of Herbs Intervening in Copper Death.

Objective: The objective of this study was to explore the medicinal properties of herbal medicines that can interfere with the copper death pathway. Methods: The Human Gene Database, Chemical Interactions in Comparative Toxicogenomics Database, Encyclopedia of Traditional Chinese Medicine, China Medical Information Platform, and Cytoscape software were used to find target and chemicals that interfere with copper death targets, as well as herbal medicines containing these chemicals and their four natures and five flavors (basic properties of herbal medicines). Results: 27 copper death-related targets were finally retrieved, as well as 2143 chemicals that could interfere with them, including 180 herbal compounds. The compounds with the highest degree values (number of nodes connected to this node) were folic acid, resveratrol, and quercetin. The 180 compounds were related to 278 herbs; those with the highest degree values (number of nodes connected to this node) were Jujubae Fructus, Ginkgo biloba L, and Acanthopanax senticosus. The 27 copper death targets were indirectly associated with 278 herbs; those with the highest degree values (number of nodes connected to this node) were Achyranthis Bidentatae Radix, Polygonum cuspidatum Sieb. et Zucc, and Mori Folium. Among the 278 herbs, 6 had incomplete information. A pharmacological analysis showed that among the 272 Chinese herbs, the most frequent meridians were the liver (133), lung (104), and spleen (91). Of the four natures, the most frequent were cold (73), warm (68), and flat (45). Of the five flavors, the most frequent were bitter (165), pungent (116), and sweet (99). Conclusion: This study preliminarily discussed the material basis and medicinal properties of herbs that can intervene in copper death, which can provide reference for the theoretical discussion, drug development, and clinical research of Chinese medicine regulating copper death.

Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study.

Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.

Clinical application and feasibility of utilizing the PEDI-CAT to assess activity and participation among children receiving physical therapy incorporating hippotherapy.

BACKGROUND: Hippotherapy (HPOT) is a physical therapy (PT) treatment tool using equine movement to improve mobility for children with movement impairments. Although research suggests HPOT improves body structure and function, there is limited evidence regarding its impact on activity and participation outcomes in a clinical setting. The Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT) may be useful in HPOT settings to highlight changes in activity and participation. PURPOSE: 1) Evaluate the PEDI-CAT's sensitivity to changes in activity and participation among children receiving PT using HPOT; 2) determine feasibility of administering the PEDI-CAT in a HPOT setting; and 3) examine how PEDI-CAT scores influence clinical decision-making. METHODS: Participants (N = 34) were children who attended weekly PT using HPOT for 6 months. The PEDI-CAT was completed for all participants by a parent or caregiver at initial treatment (T1) and 6 months later (T2). A linear mixed effects model was used to evaluate changes in scores over time. Team meetings occurred monthly to discuss how PEDI-CAT scores impacted treatment. RESULTS: There were significant improvements across 3 PEDI-CAT domains between T1 and T2 for all children with small effect sizes and nonsignificant changes noted within two diagnostic subgroups with small-to-medium effect sizes. The PEDI-CAT was completed by all participants without interrupting treatment flow. PEDI-CAT score reports enriched therapist-client conversations increasing shared decision-making. CONCLUSION: PTs who treat children using HPOT may feasibly use the PEDI-CAT to assess changes in activity level outcomes and to assist clinical decision-making.

Comparison of pain perception between computer-controlled local anesthetic delivery and the conventional syringe for inferior alveolar nerve block in children.

BACKGROUND: Local anesthesia (LA) is commonly used for pain control in clinical dental practice. However, it is often perceived as the most painful part of the treatment and the factor leading to the avoidance of dental care. Hence, research on better means of pain management is being conducted. OBJECTIVES: The aim of the study was to evaluate and compare pain perception using the No Pain III™ computer-controlled local anesthesia delivery (CCLAD) system and the conventional syringe, for inferior alveolar nerve block (IANB) in children. MATERIAL AND METHODS: Thirty children aged 6-12 years were included in the study. Children were randomly allocated into 2 groups by the flip of a coin. Group A received LA by conventional syringe and group B received LA by No Pain III™, on the contralateral side. Physiological parameters including blood pressure (BP), heart rate (HR) and respiratory rate (RR) were assessed at baseline, during the deposition and after the deposition of LA. A subjective evaluation of pain perception was assessed using the Wong-Baker FACES Pain Rating Scale (WBS). The measured values were subjected to statistical analysis. RESULTS: A statistically significant difference was observed between group A and group B for pain perception using the WBS, systolic BP and RR. CONCLUSIONS: The use of the No Pain III™ CCLAD system resulted in reduced pain perception and better acceptance when compared to the use of the conventional syringe, for IANB in children.

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review.

In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective. Potato is among the top consumed plants in the world, in general, and in developing countries, in particular. However, potato is affected by different kinds of diseases which minimize their yield and quantity. The advancement in AI and machine learning has paved the way for new methods of tackling plant disease detection. This study presents a comprehensive systematic literature review on the major diseases that harm potato crops. In this effort, computer vision-based techniques are employed to identify potato diseases, and types of machine learning algorithms used are surveyed. In this review, 39 primary studies that have provided useful information about the research questions are chosen. Accordingly, the most common potato diseases are found to be late blight, early blight, and bacterial wilt. Furthermore, the review discovered that deep learning algorithms were more frequently used to detect crop diseases than classical machine learning algorithms. Finally, the review categorized the state-of-the-art algorithms and identifies open research problems in the area.

Privacy and Trust in pHealth - Past, Present and Future.

From beginning to today, pHealth has been a data driven service that collects and uses personal health information (PHI) for personal health services and personalized healthcare. As a result, pHealth services use intensively ICT technology, sensors, computers and mathematical algorithms. In past, pHealth applications were focused to certain health or sickness related problem, but in today they use mobile devices, wireless networks, Web-technology and Cloud platforms. In future, pHealth uses information systems that are highly distributed, dynamic, increasingly autonomous, multi-stakeholder data driven eco-system having ability to monitor anywhere person's regular life, movements and health related behaviours. Because privacy and trust are pre-requirements for successful pHealth, this development raises huge privacy and trust challenges to be solved. Researchers have shown that current privacy approaches and solutions used in pHealth do not offer acceptable level of privacy, and trust is only an illusion. This indicates, that today's privacy models and technology shall not be moved to the future pHealth. The authors have analysed interesting new privacy and trust ideas published in journals, and found that they seem to be effective but offer only a partial solution. To solve this weakness, the authors used a holistic system view to aspects impacting privacy and trust in pHealth, and created a template that can be used in planning and development future pHealth services. The authors also propose a tentative solution for future trustworthy pHealth. It combines privacy as personal property and trust as legal binding fiducial duty approaches, and uses a Blockchain-based smart contract solution to store person's privacy and trust requirements and service providers' promises.