Artificial Intelligence Detection Research Articles

Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.

Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.

We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.

Role of artificial intelligence in early detection of congenital heart diseases in neonates.

In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015-2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

A Holistic Overview of Artificial Intelligence in Detection, Classification and Prediction of Atrial Fibrillation Using Electrocardiogram: A Systematic Review and Meta-analysis

A Holistic Overview of Artificial Intelligence in Detection, Classification and Prediction of Atrial Fibrillation Using Electrocardiogram: A Systematic Review and Meta-analysis

Endoskopische Diagnostik und Therapie von Adenokarzinomen des Ösophagus

The incidence of esophageal adenocarcinoma is rising worldwide. Barrett’s esophagus (BE) is the major risk factor. Diagnostic criteria are histopathological diagnosis of intestinal metaplasia with goblet cells and a minimal length of 1 cm. Gastroscopy with high-definition video quality and (virtual) chromo-endoscopy is recommended. Artificial intelligence for detection, characterization, and indicating biopsy site of suspicious lesions is currently being intensively evaluated and will hopefully be broadly available soon. Treatment of Barrett’s associated lesions is dependent on histology. For BE without dysplasia, endoscopic surveillance is recommended. BE with low-grade dysplasia can either be ablated (radiofrequency ablation [RFA]/argon plasma coagulation [APC]) or followed with endoscopic surveillance. Visible lesions representing high-grade dysplasia or early cancer should be treated with endoscopic resection (endoscopic mucosal resection [EMR]/endoscopic submucosal dissection [ESD]) followed by thermal ablation (RFA/APC) of the residual BE. This treatment is recommended for low-risk T1b adenocarcinomas (sm1 < 500ym, L0, V0, G1/G2, VM0-R0, where sm1: submucosal invasion limited to the mucosa or submucosa, L0: no lymphatic invasion, V0: no venous invasion, VM: vertical margin).

Accuracy of artificial intelligence in the detection and segmentation of oral and maxillofacial structures using cone-beam computed tomography images: a systematic review and meta-analysis

The aim of the present systematic review and meta-analysis was to resolve the conflicts on the diagnostic accuracy of artificial intelligence systems in detecting and segmenting oral and maxillofacial structures using cone-beam computed tomography (CBCT) images.We performed a literature search of the Embase, PubMed, and Scopus databases for reports published from their inception to 31 October 2022. We included studies that explored the accuracy of artificial intelligence in the automatic detection or segmentation of oral and maxillofacial anatomical landmarks or lesions using CBCT images. The extracted data were pooled, and the estimates were presented with 95% confidence intervals (CIs).In total, 19 eligible studies were identified. As per the analysis, the overall pooled diagnostic accuracy of artificial intelligence was 0.93 (95% CI: 0.91-0.94). This rate was 0.93 (95% CI: 0.89-0.96) for anatomical landmarks based on 7 studies and 0.92 (95% CI: 0.90-0.94) for lesions according to 12 reports. Moreover, the pooled accuracy of detection and segmentation tasks for artificial intelligence was 0.93 (95% CI: 0.91-0.94) and 0.92 (95% CI: 0.85-0.95) based on 14 and 5 surveys, respectively.Excellent accuracy was observed for the detection and segmentation objectives of artificial intelligence using oral and maxillofacial CBCT images. These systems have the potential to streamline oral and dental healthcare services.

KL-1: HYPERTENSION: PAST PROGRESS, PRESENT CHALLENGES AND FUTURE PROMISES

Hypertension has been long recognized as a major health challenge. Hypertension research and management have made important progress with considerable success in reducing cardiovascular morbidity and mortality. This progress is the result of basic and translational research in developing animal models, the fundamental understanding of physiology and biochemistry of blood pressure regulation, and the development of therapeutics. More recently, advances in genetics, genomics, molecular pathways, and emerging technology show much promise. However, there remains substantial challenges. Hypertension is controlled in less than a fifth of patients worldwide. There are vast global disparities in hypertension awareness, treatment, and control, especially problematic in low- and middle-income countries. Challenges include early detection and follow-up that is dependent on cuff-based measurement and drug regimens, which are often not precise, and have side effects. In spite of recent scientific progress and numerous clinical research trials, recent research has not yielded major advances in hypertension control. Transformation is urgently needed to reduce the global burden of hypertension. The future will depend on effective development of digital technologies, insights from data science and artificial intelligence for detection and management, new techniques such as genome editing which provide potential cures, and the importance of population science for global control.

Lung CAncer SCreening in French women using low-dose CT and Artificial intelligence for DEtection: the CASCADE study protocol

IntroductionLung cancer screening (LCS) using low-dose CT has been demonstrated to reduce lung cancer-related mortality in large randomised controlled trials. Moving from trials to practice requires answering practical questions about...

Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities

BackgroundWith the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders.MethodsThis is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software.DiscussionThis study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews.Trial registrationPROSPERO, CRD42021274441

FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Fire outbreaks are a typical occurrence all throughout the world, and the harm they bring to both nature and humans is enormous. In comparison to classic sensor-based fire detection systems, vision-based fire detection systems have recently gained favour. The detection process using image processing techniques, on the other hand, is extremely time consuming. We designed a Convolutional Neural Network-based fire detection technique. Through training with datasets, obtain high-accuracy fire image detection that is consistent with fire detection. In this paper, we propose a system for detecting fire using Convolutional Neural Networks (CNN). This paper critically analyzes the scope of Artificial intelligence for detection with video from CCTV footages. This project uses dataset containing video frames with fire. The data is then preprocessed and use the CNN to build a model to detect fire. The dataset is given as input for validating the algorithm and experiments are noted. This project focus on building high accuracy and cost efficient machine that can be used for fire detection.There are 755 and 244 images in the datasets for fire and non-fire, respectively. There are 999 photos in all. These images were created using fire-related video and some images found on the internet. There are 999 images that have been scaled and reshaped to convert to a training dataset and 999 images that have been resized and reshaped to convert to a testing dataset. Convolution, activation functions, and the max pooling technique are used to train the model. The model is trained by varying batch sizes and epoch values. As a result, we obtain a high accuracy and detection rate. Key words: CNN,fire detection,machine learning.

Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations.

To develop and validate an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect different patterns of fetal intracranial abnormality in standard sonographic reference planes for screening for congenital central nervous system (CNS) malformations. Neurosonographic images from normal fetuses and fetuses with CNS malformations at 18-40 gestational weeks were retrieved from the databases of two tertiary hospitals in China and assigned randomly (ratio, 8:1:1) to training, fine-tuning and internal validation datasets to develop and evaluate the PAICS. The system was built based on a real-time convolutional neural network (CNN) algorithm, You Only Look Once, version 3 (YOLOv3). An image dataset from a third tertiary hospital was used to further validate, externally, the performance of the PAICS and to compare its performance with that of sonologists with different levels of expertise. Furthermore, a prospective video dataset was employed to evaluate the performance of the PAICS in a real-time scan scenario. The diagnostic accuracy, sensitivity, specificity and area under the receiver-operating-characteristics curve (AUC) were calculated to assess the performance of the PAICS and to compare this with the performance of sonologists with different levels of experience. In total, 43 890 images from 16 297 pregnancies and 169 videos from 166 pregnancies were used to develop and validate the PAICS. The system achieved excellent performance in identifying 10 types of intracranial image pattern, with macro- and microaverage AUCs, respectively, of 0.933 (95% CI, 0.798-1.000) and 0.977 (95% CI, 0.970-0.985) for the internal validation image dataset, 0.902 (95% CI, 0.816-0.989) and 0.898 (95% CI, 0.885-0.911) for the external validation image dataset and 0.969 (95% CI, 0.886-1.000) and 0.981 (95% CI, 0.974-0.988) in the real-time scan setting. The performance of the PAICS was comparable to that of expert sonologists in terms of macro- and microaverage accuracy (P = 0.863 and P = 0.775, respectively), sensitivity (P = 0.883, P = 0.846) and AUC (P = 0.891, P = 0.788), but required significantly less time (0.025 s per image for PAICS vs 4.4 s for experts, P < 0.001). Both in the image dataset and in the real-time scan setting, the PAICS achieved excellent diagnostic performance for various fetal CNS abnormalities. Its performance was comparable to that of experts, but it required less time. A CNN algorithm can be trained to detect fetal CNS abnormalities. The PAICS has the potential to be an effective and efficient tool in screening for fetal CNS malformations in clinical practice. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

Application of Artificial Intelligence in detection of diseases in plants: A Survey

The agriculture sector can be considered as the backbone for any developing economy. To obtain the maximum yield from the crops, it is required that farmers should be provided with the best technologies and methodologies. Artificial intelligence is having its vast applications in various sectors. Due to its ability to perceive the problems, developing the appropriate reasons for that and to establish optimal solutions for it, artificial intelligence can act as a great aid in addressing the diseases of crops.  The paper presents a brief overview of the application of artificial intelligence in agriculture, its available techniques for agriculture and highlights the various methods available for the detection of diseases in crops

Role of Artificial Intelligence In Detection of COVID-19: A Review

The corona virus is world’s pandemic disease originated in China 2019 thus called Corona Virus Disease 2019 (COVID-19). It surprised the world with its rapid spread and had major impact on human life as well as on global market. The fundamental solution to control the rapid spread of disease is social distance and home quarantine, but no one can stay at leisure for longer time. Thus, we have to live with COVID-19 by preventing ourselves. Symptomatic patients were called to test for COVID-19 immediately and will be quarantined. But asymptomatic patients become strange for the society today. To overcome this issue some intelligent system test is essential. Collection of patients sample itself is a risky. Thus, in preliminary step, Artificial Intelligence (AI) plays a vital role for detection of COVID-19. If the process of detection include an Artificial Intelligence it becomes easier for corona warriors to take necessary action on this pandemic and can able to detect disease at the earliest. Thus, in this article we have reviewed role of Artificial Intelligence in detection of COVID -19. The review resulted as there is urgent need of developing AI-based system for early detection, tracking and quantification of corona virus patients to save the world from this pandemic disease.

Application of artificial intelligence in prevention and detection of Covid- 19

Artificial intelligence (AI) and its applications have assisted in many areas of COVID-19 and are playing a very crucial role in its management. The present article mentions usefulness of Artificial intelligence in detection and management of cases of COVID- 19.

Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques.

Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital features without human intervention. The automatic detection and segmentation of subtle tissue such as the internal auditory canal (IAC) and its nerves is a challenging task, and it can be improved using deep learning techniques. The main scope of this research is to present an automatic method to detect and segment the IAC and its nerves like the facial nerve, cochlear nerve, inferior vestibular nerve, and superior vestibular nerve. To address this issue, we propose a Mask R-CNN approach driven with U-net to detect and segment the IAC and its nerves. The Mask R-CNN with its backbone network of the RESNET50 model learns a background-based localization policy to produce an actual bounding box of the IAC. Furthermore, the U-net segments the structure related information of IAC and its nerves by learning its features. The proposed method was experimented on clinical datasets of 50 different patients including adults and children. The localization of IAC using Mask R-CNN was evaluated using Intersection of Union (IoU), and segmentation of IAC and its nerves was evaluated using Dice similarity coefficient. The localization result shows that mean IoU of RESNET50, RESNET101 are 0.79 and 0.74, respectively. The Dice similarity coefficient of IAC and its nerves using region growing, PSO and U-net method scored 92%, 94%, and 96%, respectively. The result shows that the proposed method outperform better in localization and segmentation of IAC and its nerves. Thus, AI aids the radiologists in making the right decisions as the localization and segmentation of IAC is accurate.

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n -order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

384 High Accuracy and Effectiveness With Deep Neural Networks and Artificial Intelligence in Detection of Early Esophageal Neoplasia in Barrett's Esophagus

INTRODUCTION: The visual detection of early esophageal neoplasia (high grade dysplasia and T1 cancer) in BE with white light and virtual chromoendoscopy is still difficult. The aim of this study is to assess if a convolutional neural artificial intelligence network can aid in the recognition of early esophageal neoplasia in BE. METHODS: Over 800 images from 65 patients were retrospectively collected of histology-proven early esophageal neoplasia in BE containing high grade dysplasia or T1 cancer (Dysplasia Group). Within each image, the area of neoplasia was masked using image annotation software by two experts in BE imaging. Over 800 control images were collected of either histology-proven or confocal endomicroscopy-proven BE without high grade dysplasia (Non -Dysplastic Group). A training set with ∼1200 images split 50/50 Dysplasia/Non-Dysplasia was used to train the algorithm. We used a convolutional neural network (CNN) and hybrid algorithm design including Inception blocks to deepen the neural net and maximize efficiency and accuracy. The algorithm was pre-trained on ImageNet and then fine-tuned with the goal to provide the correct binary classification: “Dysplastic” (1) or “Non-dysplastic” (0). Adam optimizer performed stochastic optimization of a binary cross-entropy loss function to produce a probability value between 0 and 1. A set 458 images unique of the training set was used for algorithm validation. We additionally developed an object detection algorithm which drew localization boxes in real-time around regions classified as dysplasia. Testing was performed for near-focus images, non-near focus (far) images, white light and NBI images. RESULTS: The CNN analyzed 458 test images (225 dysplasia/233 non-dysplasia) and correctly detected early neoplasia in BE cases with sensitivity of 95.6% and specificity of 91.8%. The accuracy was 93.7% and the AUC was 0.94 (Figure 3). With regards to the object detection algorithm for all images in the validation set, the system was able to achieve a mean-average-precision (mAP) of 0.7533 at an intersection over union (IOU) of 0.3, Sensitivity 96.7% and Specificity 87.6% (Figure 1). CONCLUSION: Our AI model was able to detect early esophageal neoplasia in Barrett’s Esophagus images with 93.7% accuracy. In addition, the object detection algorithm was able to draw a localization box around the areas of dysplasia with high precision.