Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.

Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.

Association between Gingival Phenotype and Periodontal Disease Severity-A Comparative Longitudinal Study among Patients Undergoing Fixed Orthodontic Therapy and Invisalign Treatment.

This longitudinal study aimed to compare the association between gingival phenotype (thin vs. thick) and periodontal disease severity in patients undergoing fixed orthodontic therapy (FOT) and Invisalign treatment over a six-month follow-up period. Clinical periodontal parameters, including full mouth plaque score (FMPS), full mouth bleeding score (FMBS), gingival index (GI), probing pocket depth (PPD), clinical attachment loss (CAL), gingival recession (GR), keratinized tissue width (KTW), transgingival probing, and gingival biotype assessment, were recorded at baseline and 6 months into treatment for both orthodontic groups and a control group. Statistical analysis evaluated differences in parameters between groups and across time points. In the thick phenotype, both Invisalign and FOT groups showed a significant mean reduction in FMPS (baseline to 6 months) by -24.8707 and -12.3489, respectively (p < 0.05). The gingival index decreased significantly for both groups, with Invisalign and FOT showing reductions of -0.83355 and -1.10409, respectively (p < 0.05). FMBS (baseline to 6 months) decreased significantly for Invisalign and FOT, with mean differences of -9.10298 and -12.6579 (p < 0.05). Probing pocket depth (baseline to 6 months) was also significantly reduced for both Invisalign and FOT groups while CAL showed non-significant differences in both groups (p > 0.05). Similar changes were seen in the thin phenotype too. This study highlights the positive influence of both Invisalign and fixed orthodontic therapy on periodontal health, particularly in patients with thin and thick gingival biotypes. These findings, with significant reductions in key periodontal parameters, offer valuable insights to guide orthodontic treatment decisions and enhance patient outcomes.

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security.

Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.

A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity.

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods.

An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index.

Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliable drought forecast model and figuring out how well various models can analyze drought factors in coastal arid regions are two of the biggest obstacles in this field. Different time-series methods and machine-learning models have traditionally been utilized in forecasting strategies. Deep learning is promising when describing the complex interplay between coastal drought and its contributing variables. Considering the possibility of enhancing our understanding of drought features, applying deep learning approaches has yet to be tried widely. The current investigation employs a deep learning strategy. Coastal Drought indices are commonly used to comprehend the situation better; hence the Standard Precipitation Evaporation Index (SPEI) was used since it incorporates temperatures and precipitation into its computation. An integrated coastal drought monitoring model was presented and validated using convolutional long short-term memory with self-attention (SA-CLSTM). The Climatic Research Unit (CRU) dataset, which spans 1901-2018, was mined for the drought index and predictor data. To learn how LSTM forecasting could enhance drought forecasting, we analyzed the findings regarding numerous drought parameters (drought severity, drought category, or geographic variation). The model's ability to predict drought intensity was assessed using the Coefficient of Determination (R2), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). Both the SPEI 1 and SPEI 3 examples had R2 values more than 0.99 for the model. The range of predicted outcomes for each drought group was analyzed using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) method. The research showed that the AUC for SPEI 1 was 0.99 and for SPEI 3, 0.99. The study's results indicate progress over machine learning models for one month in advance, accounting for various drought conditions. This work's findings may be used to mitigate drought, and additional improvement can be achieved by testing other models.

A novel automated Parkinson's disease identification approach using deep learning and EEG.

The neurological ailment known as Parkinson's disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson's disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study's suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.

Impact of telepharmacy on patients’ outcome during COVID-19: a systematic literature review

Background: Coronavirus disease (COVID-19) continues to be a major global public health issue. COVID-19 is highly contagious, and numerous mitigation strategies have recently been implemented to prevent the spread of this disease. Pharmacists utilize telecommunication technology to provide patient care services, thus increasing patient access to pharmaceutical services. There was a scarcity of evidence regarding the impact of telepharmacy on patient outcomes during COVID-19. Therefore, the aim of this study was to summarize the available research evidence on the impact of telepharmacy on patient outcomes during COVID-19. Methods: A systematic literature search was conducted between January 2020 to September 2022 in Ovid MEDLINE, Ovid Embase, and Cochrane Central Register of Controlled Trials, using appropriate terms on telepharmacy, COVID-19, and patient outcomes. Only studies that investigated the impact of telepharmacy on patient outcomes during COVID-19 were included. A systematic literature search was conducted between January 2020 to September 2022 in Ovid MEDLINE, Ovid Embase, and Cochrane Central Register of Controlled Trials, using appropriate terms on telepharmacy, COVID-19, and patient outcomes. Only studies that investigated the impact of telepharmacy on patient outcomes during COVID-19 were included. Results: A total of three studies were included in the review. The telepharmacy services were offered via virtual anticoagulation clinic, retail community telepharmacy through information technology tools, and RxLive® telepharmacy program. All studies included in the review demonstrated that the provision of telepharmacy services during COVID-19 had an overall positive impact on the patient outcomes such as a reduction in the rates of hospitalisation and medication-related problems and maintaining the international normalized ratio values within the therapeutic range (AU)

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

Intelligent Localization and Deep Human Activity Recognition through IoT Devices.

Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system's accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system's performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject's location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network.

Gestures have been used for nonverbal communication for a long time, but human-computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected through the images, and the skeleton is computed through mathematical computations. From the skeleton, the features are extracted; these features include joint color cloud, neural gas, and directional active model. After that, the features are optimized, and a selective feature set is passed through the classifier recurrent neural network (RNN) to obtain the classification results with higher accuracy. The proposed model is experimentally assessed and trained over three datasets: HaGRI, Egogesture, and Jester. The experimental results for the three datasets provided improved results based on classification, and the proposed system achieved an accuracy of 92.57% over HaGRI, 91.86% over Egogesture, and 91.57% over the Jester dataset, respectively. Also, to check the model liability, the proposed method was tested on the WLASL dataset, attaining 90.43% accuracy. This paper also includes a comparison with other-state-of-the art methods to compare our model with the standard methods of recognition. Our model presented a higher accuracy rate with a markerless approach to save money and time for classifying the gestures for better interaction.

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework.

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system's results that an acceptable mean accuracy rate of 84.14% has been achieved.

Orthodontic camouflage treatment of pseudo-Class III malocclusion on skeletal class III Base complicated with canine impaction treated with temporary anchorage devices: A case report.

With conventional mechanics to protract the upper posterior teeth for correcting Class III molar relationships, such as intra-arch mechanics, face mask reverse-pull headgear protraction, and interarch Class III elastics, there are some adverse effects, including diminished patient compliance, the possibility of losing anchorage, and extrusion of upper molars and lower incisors with counterclockwise rotation of the occlusal plane. Protraction force should be directed through the center of resistance of the upper posterior teeth to prevent these side effects.

Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder.

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug-drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.

Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT.

This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.

Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images.

Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

Association between Chronological Age and IGF-1, IGFBP-3, and CTX Levels in Saliva of Children through Younger Adult Population with Varying Periodontal Status.

The quest for the most precise and non-invasive technology to monitor the pubertal growth spurt is driven by the role of growth determination in orthodontics. The objective of this study was to estimate the levels of salivary insulin-like growth factor-1 (IGF-1), IGF-binding protein-3 (IGFBP-3), and cross-linked C-terminal telopeptide of type I collagen (CTX1), and to analyze whether the levels of these biomarkers vary among different chronological age groups with and without periodontal disease. Eighty participants were divided into three groups based on their chronological age: group 1: 6−12 years; group 2: 13−19 years; and group 3: 20−30 years. The assessed clinical parameters included the simplified oral hygiene index (OHI-S), bleeding on probing (BOP), probing pocket depth (PPD), clinical attachment loss (CAL), and community periodontal index (CPI). Using ELISA kits, the IGF-1, IGFBP-3, and CTX1 levels in the saliva samples were estimated. The salivary concentration of IGFBP-3 was significantly associated with age and gender (p < 0.01). However, no significance was observed between subjects with and without periodontal disease. Significant associations existed between the values of IGF-1, IGFBP-3, and CTX1 in saliva among subjects from the various chronological age groups. Estimation of salivary IGF-1 and IGFBP-3 could serve as a useful tool in the assessment of growth maturity and bone remodeling patterns during orthodontic treatment planning.

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.

Correlation between Salivary Levels of IGF-1, IGFBP-3, IGF-1/IGFBP3 Ratio with Skeletal Maturity Using Hand-Wrist Radiographs.

Objective: The relevance of growth determination in orthodontics is driving the search for the most precise and least invasive way of tracking the pubertal growth spurt. Our aim was to explore whether minimally invasive salivary estimation of biomarkers Insulin-like growth factor (IGF-1) and Insulin-like growth factor binding protein-3 (IGFBP-3) could be used to estimate skeletal maturity for clinical convenience, especially in children and adolescent age groups. Materials and Method: The cross-sectional study was conducted on 90 participants (56 girls and 34 males) with ages ranging from 6 to 25 years. Each subject's hand-wrist radiograph was categorized based on skeletal maturity, and saliva samples were estimated for IGF-1 and IGFBP-3 using the respective ELISA kits. Kruskal−Wallis nonparametric ANOVA was applied to compare different skeletal stages. Results: The study demonstrated low salivary IGF-1 levels at the prepubertal stage, with increase during pubertal onset and peak pubertal stage followed by a decline during pubertal deceleration to growth completion. Spearman's correlation coefficient demonstrated a strong positive association (r = 0.98 p < 0.01) between salivary IGF/IGFBP-3 ratio and different stages of skeletal maturity. Conclusion: Salivary IGF-1, IGFBP-3, and IGF/IGFBP-3 ratio could serve as a potential biochemical marker for predicting the completion of skeletal maturity.