Convolutional Neural Network System Research Articles

Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.

Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings. Initially, a CNN was developed to classify lung diseases using X-ray images, distinguishing between healthy individuals and COVID-19 patients. Later, the model was expanded to include pneumonia patients. To evaluate performance, numerous experiments were conducted with varied data sizes and imbalance ratios for both binary and ternary classifications, measuring various indices to validate the model's efficacy. The study revealed that increasing dataset size positively impacts CNN performance, but this improvement saturates beyond a certain size. A novel finding is that the data balance ratio influences performance more significantly than dataset size. The behavior of three-class classification mirrored that of binary classification, underscoring the importance of balanced datasets for accurate classification. This study emphasizes the fact that achieving balanced representation in datasets is crucial for optimal CNN performance in healthcare, challenging the conventional focus on dataset size. Balanced datasets improve classification accuracy, both in two-class and three-class scenarios, highlighting the need for data-balancing techniques to improve model reliability and effectiveness. Our study is motivated by a scenario with 100 patient samples, offering two options: a balanced dataset with 200 samples and an unbalanced dataset with 500 samples (400 healthy individuals). We aim to provide insights into the optimal choice based on the interplay between dataset size and imbalance, enriching the discourse for stakeholders interested in achieving optimal model performance. Recognizing a single model's generalizability limitations, we assert that further studies on diverse datasets are needed.

The educational resource management based on image data visualization and deep learning

In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.

Age and Gender voice Recognition using Deep learning

Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Abstract Convolutional neural networks (CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects (SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm system-on-chip (SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

CONVOLUTIONAL NEURAL NETWORK (CNN) BASED ON ARTIFICIAL INTELLIGENCE IN PERIODONTAL DISEASES DIAGNOSIS

Introduction: The main problem by many clinicians is the correct diagnosis of periodontal disease. Usually, conventional clinical measurements such as measuring probing depth, attachment loss, presence of plaque and calculus are the way to diagnose and classify periodontal disease. However, clinical examination has limited reliability for periodontitis screening. Likewise, the ability of dentists to read radiographs using conventional methods increases the risk of misdiagnosis. Due to the diversity of existing clinical criteria and the increase in knowledge about human health, changes in the diagnostic criteria for periodontal disease that have occurred in recent decades have led to several updates. Recent research has focused on developing artificial intelligence tools to assist in diagnostic and therapeutic roles. This literature review aims to determine the use of artificial intelligence-based convolutional neural network (CNN) in diagnosing periodontal disease. Review: Artificial intelligence (AI) can make more accurate and efficient diagnoses, thereby reducing the workload of dentists. The use of the convolutional neural network (CNN) system in diagnosis and treatment planning allows dentists to reduce diagnostic errors that arise. Several studies have found that the CNN algorithm can assist in detecting alveolar bone loss, gingival abnormalities, and assisting early intervention in implantology. The CNN system can also capture details that dentists miss in diagnosis, especially radiographic diagnosis. Conclusion: Implementing an AI system is effective in helping to analyze periodontal disease. The CNN algorithm outperforms other AI techniques that can be used to facilitate diagnosis and treatment planning by dentists in the future.

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks.

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising approach for enhancing DR diagnosis. OCT provides detailed retinal morphology information, while CNNs analyze OCT images for automated detection and classification of DR. This paper reviews the current research on OCT imaging and CNNs for DR diagnosis, discussing their technical aspects and suitability. It explores CNN applications in detecting lesions, segmenting microaneurysms, and assessing disease severity, showing high sensitivity and accuracy. CNN models outperform traditional methods and rival expert ophthalmologists' results. However, challenges such as dataset availability and model interpretability remain. Future directions include multimodal imaging integration and real-time, point-of-care CNN systems for DR screening. The integration of OCT imaging with CNNs has transformative potential in DR diagnosis, facilitating early intervention, personalized treatments, and improved patient outcomes. Abbreviations: DR = Diabetic Retinopathy, OCT = Optical Coherence Tomography, CNN = Convolutional Neural Network, CMV = Cytomegalovirus, PDR = Proliferative Diabetic Retinopathy, AMD = Age-Related Macular Degeneration, VEGF = vascular endothelial growth factor, RAP = Retinal Angiomatous Proliferation, OCTA = OCT Angiography, AI = Artificial Intelligence.

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Aims: This study aimed to investigate the use of a convolutional neural network (CNN) deep learning approach to accurately identify total knee arthroplasty (TKA) implants from X-ray radiographs.
 Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69% of the data, with the remaining 31% in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance.
 Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100% identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100% for each individual implant model.
 Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AI’s ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different systems. We compared endoscopist readings using an existing and a novel CNN system in a real-world SBCE setting. Thirty-six complete SBCE videos, including 43 abnormal lesions (18 mucosal breaks, 8 angioectasia, and 17 protruding lesions), were retrospectively prepared. Three reading processes were compared: (A) endoscopist readings without CNN screening, (B) endoscopist readings after an existing CNN screening, and (C) endoscopist readings after a novel CNN screening. The mean number of small-bowel images was 14747 per patient. Among these images, existing and novel CNN systems automatically captured 24.3% and 9.4% of the images, respectively. In this process, both systems extracted all 43 abnormal lesions. Next, we focused on the clinical usefulness. The detection rates of abnormalities by trainee endoscopists were not significantly different across the three processes: A, 77%; B, 67%; and C, 79%. The mean reading time of the trainees was the shortest during process C (10.1min per patient), followed by processes B (23.1min per patient) and A (33.6min per patient). The mean psychological stress score while reading videos (scale, 1-5) was the lowest in process C (1.8) but was not significantly different between processes B (2.8) and A (3.2). Our novel CNN system significantly reduced endoscopist reading time and psychological stress while maintaining the detectability of abnormalities. CNN performance directly affects clinical utility and should be carefully assessed.

Oral cancer detection using convolutional neural network optimized by combined seagull optimization algorithm

Early diagnosis of oral cancer is crucial for improving patient outcomes and saving lives. However, inaccurate and improper diagnosis can hinder effective treatment. This paper presents a novel method for detecting oral cancer using an optimized version of Convolutional Neural Network (CNN). While basic CNNs have been widely used for image classification tasks, the incorporation of the Seagull Optimization Algorithm and Particle Swarm Optimization Algorithm in optimizing the CNN architecture specifically for oral cancer detection is a unique approach that is provided in this study. By combining these algorithms, the proposed method optimizes the CNN's architecture, parameters, and training process specifically for oral cancer detection. This optimization enhances the performance and accuracy of the CNN in identifying cancerous regions in oral images. Unlike previous approaches, our method incorporates advanced image processing techniques, including noise reduction, contrast enhancement, and data augmentation, to enhance the quality of input data extracted from the Oral Cancer (Lips and Tongue) images (OCI) dataset. The optimized CNN architecture uses its ability to learn intricate patterns and features from the enhanced images, enabling more accurate identification of cancerous regions. To evaluate the effectiveness of our approach, we compare it against Textural analysis, FCM, CNN, R-CNN, and ResNet-101 using four measurement indices. Results demonstrate that our proposed CSOA-based CNN system achieves the highest accuracy rate (96.94%) compared to other methods, indicating its superior performance in oral cancer detection. Furthermore, our precision rate of 94.65% and recall rate of 91.60% highlight the model's high correctness and positive classification ability. Finally, our proposed method achieves the highest F1-score (88.55%), emphasizing its superiority over other comparative methods. Through our innovative integration of the Seagull Optimization Algorithm and Particle Swarm Optimization Algorithm with CNN, coupled with advanced image processing techniques, we provide a reliable and effective solution for early detection of oral cancer.

A comparison of data augmentation methods in voice pathology detection

To distinguish pathological voices from healthy voices, automatic voice pathology detection systems can be built using machine learning (ML) and deep learning (DL) techniques. To fully exploit such systems, large quantities of training data are typically required. The amount of training data is, however, small in the area of pathological voice, and therefore data augmentation (DA) becomes a potential technology to artificially increase the quantity of training data. This study presents a systematic comparison between various DA methods in the detection of pathological voice, including three time domain methods (noise addition, pitch shifting and time stretching), one time–frequency domain method (SpecAugment), and two vocoder-based methods (harmonic-to-noise ratio (HNR) modification and glottal pulse length modification). Detection systems were built using four popular spectral feature representations (static mel-frequency cepstral coefficients (MFCCs), dynamic MFCCs, spectrogram and mel-spectrogram). As classifiers, two widely used ML models (support vector machine (SVM) and random forest (RF)) and two DL models (long short-term memory (LSTM) network and convolutional neural network (CNN) with 1-dimensional (1-D) and 2-dimensional (2-D) architectures) were used. These systems were trained using a small number of training samples from two popular databases of pathological voice (HUPA and SVD) to find the best feature/classifier combination for each database. As a result, one ML-based detection system (mel-spectrogram/SVM for HUPA and SVD) and two DL-based detection systems (dynamic MFCCs/2-D CNN for HUPA and mel-spectrogram/2-D CNN for SVD) were selected for the comparison of the DA methods. The results show that by using DA in the system training, detection accuracy increased compared to the baseline systems that were trained without using DA. This improvement in accuracy was, however, clearly larger for the 2D-CNN system than for the SVM system. Furthermore, all six DA methods improved accuracy of the 2-D CNN system compared to the baseline system for both databases. The highest improvements were achieved using the time–frequency domain SpecAugment DA method, which improved accuracy by 1.5% and 3.8% (absolute) for the HUPA and SVD database, respectively.

Methodics and tools of cough sound processing on basic of neural net

The purpose of the article is to analyze the methods and means of processing cough sounds to detect lung diseases, as well as to describe the developed system for classifying and detecting cough sounds based on a deep neural network. Four types of machine learning and the use of convolutional neural network (CNN) are considered. Hypermarkets of CNN are given. Varieties of machine learning based on the CNN are discussed. The analysis of works on the methodology and means of processing cough sounds based on the CNN with the reduction of the means used and the accuracy of recognition is carried out. Details of machine learning using the environmental sound classification 50 (ESC-50) dataset are discussed. To recognize COVID-19 cough, a classifier was analyzed using CNN as a machine learning model. The proposed CNN system is designed to classify and detect cough sounds based on ESC-50. After selecting a set of sound classification data, four stages are described: extraction of features from audio files, labeling, training, testing. The ESC-50 used for the study was downloaded from the Kaggle website. Python libraries and modules related to deep learning and data science were used to implement the project: NumPy, Librosa, Matplotlib, Hickle, Sci-Kit Learn, Keras. The implemented network used a stochastic gradient algorithm. Several volunteers recorded their voices while coughing using their smartphones and it was assured to record their voices in a public environment to introduce noise to the sounds, in addition to some audio files that were downloaded online. The results showed an average accuracy of 85.37 %, precision of 78.8 % and a recall record of 91.9 %.

Disease surveillance evaluation of primary small-bowel follicular lymphoma using capsule endoscopy images based on a deep convolutional neural network (with video)

Capsule endoscopy (CE) is useful in evaluating disease surveillance for primary small-bowel follicular lymphoma (FL), but some cases are difficult to evaluate objectively. This study evaluated the usefulness of a deep convolutional neural network (CNN) system using CE images for disease surveillance of primary small-bowel FL. We enrolled 26 consecutive patients with primary small-bowel FL diagnosed between January 2011 and January 2021 who underwent CE before and after a watch-and-wait strategy or chemotherapy. Disease surveillance by the CNN system was evaluated by the percentage of FL-detected images among all CE images of the small-bowel mucosa. Eighteen cases (69%) were managed with a watch-and-wait approach, and 8 cases (31%) were treated with chemotherapy. Among the 18 cases managed with the watch-and-wait approach, the outcome of lesion evaluation by the CNN system was almost the same in 13 cases (72%), aggravation in 4 (22%), and improvement in 1 (6%). Among the 8 cases treated with chemotherapy, the outcome of lesion evaluation by the CNN system was improvement in 5 cases (63%), almost the same in 2 (25%), and aggravation in 1 (12%). The physician and CNN system reported similar results regarding disease surveillance evaluation in 23 of 26 cases (88%), whereas a discrepancy between the 2 was found in the remaining 3 cases (12%), attributed to poor small-bowel cleansing level. Disease surveillance evaluation of primary small-bowel FL using CE images by the developed CNN system was useful under the condition of excellent small-bowel cleansing level.

Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection in the input image was performed using a convolutional neural network (CNN) model single shot multibox detector (SSD), which is one of the CNN models that implements a bounding box to estimate the localization of detected objects. In this case, system testing was used to evaluate the performance of the CNN system algorithm for aura detection in terms of color (main color or average color). The results obtained were detectibility by 80%, selectivity by 88.88%, precision by 70%, sensitivity by 87.5%, and accuracy by 63.63%. The design of the aura detection system in this study will make it easier for psychiatrists and psychologists to help make a noninvasive diagnosis.

Prediction of Stem Cell State Using Cell Image‐Based Deep Learning

Stem cells represent an ideal source for regenerative medicine; however, longitudinal assessment of stem cell phenotype and function is challenging. Contrastingly, a convolutional neural network (CNN) algorithm can automatically extract the image features and produce highly accurate image recognition. Thus, this study implements CNN to establish stable and reproducible cell culture experiments by predicting a unique morphology of pluripotent stem cell (PSC) lines. Interestingly, the algorithm distinguishes the PSC lines cultured in the different cell culture conditions, such as the presence or absence of small molecules and/or the long‐ or short‐term culture in our induced PSC (iPSC) models, which include iPSC lines with abnormal gene expression patterns and genomic abnormalities. Our deep learning technology accurately classifies the various cell lines with or without genetic defects using only the cell images, without any labeling process. This suggests that the CNN system may simplify the various tasks involving stable cell cultures and their differentiation.

Bird Species Identification Using CNN

In our world, there are above 11000 bird species. Some bird species are being found rarely. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, we utilised convolutional neural networks (CNN). CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. Everyone can quickly determine the name of the bird they wish to know by following this strategy. Keywords— Bird species, Image Processing, Convolutional Neural Networks.

Using image analysis to quantify defects and prioritize repairs in built-up roofs

PurposeThis paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.Design/methodology/approachIn this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.FindingsThe developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.Research limitations/implicationsFuture research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.Originality/valueThis study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.

Parallel Photonic Convolutional Processing on-Chip With Cross-Connect Architecture and Cyclic AWGs

Convolutional neural network (CNN) is one of the best neural network structures for solving classification problems. The convolutional processing of the network dominates processing time and computing power. Parallel computing for convolutional processing is essential to accelerate the computing speed of the neural network. In this paper, we introduce another domain of parallelism on top of the already demonstrated parallelisms suggested for photonic integrated processors with WDM approaches, to further accelerate the convolutional operation on chip. The operation of the novel parallelism is introduced with an updated cross-connect architecture, exploiting cyclic array waveguide grating. The photonic CNN system is demonstrated for the handwritten digit classification problem in simulation, with a speed of 2.56 Tera operation/s and end-to-end system energy efficiency of 3.75 pJ/operation, using 16 weighting elements and 10 Giga sample/s inputs. The proposed parallelism improves CNN acceleration by 4-16 times with respect to state-of-the-art integrated convolutional processors, depending on the available weighting elements per convolutional core.

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the neural network framework. Through the text convolutional network, the input layer in CNN has transformed into two channels: static ones and non-static ones. Meanwhile, the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher. The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding. It obtains data name features through a convolution kernel. Finally, the top pooling layer obtains the length vector. The attention system layer obtains the characteristics of the data type. Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage. The proposed algorithm shows excellent accuracy and robustness.

Automated System for Bird Species Identification Using CNN

There are around 11000 different bird species in the globe. Rarely are certain bird species encountered. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, the convolutional neural networks(CNN) is utilized for an automated bird species identification system. CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. This will lead to quick identification of the bird species using an automated process.

Accuracy of convolutional neural network in the diagnosis of alveolar bone loss due to periodontal disease: A systematic review and meta-analysis

Periodontitis is an inflammation of the supporting structures of teeth, involving progressive alveolar bone loss. A computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm was developed. This study aimed to assess the existing literature to determine the accuracy of the CNNs for diagnosing and measuring periodontal bone loss (PBL). We used a modified approach to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension diagnostic test accuracy by searching the following databases: Scopus, PubMed, Cochrane, and Web of Science, in addition to gray literature. Medical Subject Headings terms and Keywords in the title and abstract fields, as well as subject headings for both periodontal disease/bone loss and CNN/artificial intelligence, were used to search the existing literature for publications relevant to the evaluation of the accuracy of CNN for the detection and measurement of alveolar bone loss over the past three decades (January 1990–May 2021). Quality analysis was performed using the quality assessment and diagnostic accuracy tool-2. Four thousand six hundred and ninety potentially relevant titles and abstracts were found after an initial electronic and manual search and removal of duplicates. Applying the inclusion and exclusion criteria yielded 75 publications, which were further analyzed for relevance and applicability. Most of the included studies were observational. Following the critical analysis, eight publications were used to assess CNN’s precision of the CNN for PBL measurements. The CNN system successfully determined PBL. Therefore, it can facilitate the diagnosis and treatment planning for dentists in the future.