Computer-aided Diagnosis Tool Research Articles

Acceptability of artificial intelligence for cervical cancer screening in Dschang, Cameroon: a qualitative study on patient perspectives

BackgroundCervical cancer is the fourth most frequent cancer among women, with 90% of cervical cancer-related deaths occurring in low- and middle-income countries like Cameroon. Visual inspection with acetic acid is often used in low-resource settings to screen for cervical cancer; however, its accuracy can be limited. To address this issue, the Swiss Federal Institute of Technology Lausanne and the University Hospitals of Geneva are collaborating to develop an automated smartphone-based image classifier that serves as a computer aided diagnosis tool for cancerous lesions. The primary objective of this study is to explore the acceptability and perspectives of women in Dschang regarding the usage of a screening tool for cervical cancer relying on artificial intelligence. A secondary objective is to understand the preferred form and type of information women would like to receive regarding this artificial intelligence-based screening tool.MethodsA qualitative methodology was employed to gain better insight into the women’s perspectives. Participants, aged between 30 and 49 were invited from both rural and urban regions and semi-structured interviews using a pre-tested interview guide were conducted. The focus groups were divided on the basis of level of education, as well as HPV status. The interviews were audio-recorded, transcribed, and coded using the ATLAS.ti software.ResultsA total of 32 participants took part in the six focus groups, and 38% of participants had a primary level of education. The perspectives identified were classified using an adapted version of the Technology Acceptance Model. Key factors influencing the acceptability of artificial intelligence include privacy concerns, perceived usefulness, and trust in the competence of providers, accuracy of the tool as well as the potential negative impact of smartphones.ConclusionThe results suggest that an artificial intelligence-based screening tool for cervical cancer is mostly acceptable to the women in Dschang. By ensuring patient confidentiality and by providing clear explanations, acceptance can be fostered in the community and uptake of cervical cancer screening can be improved.Trial registrationEthical Cantonal Board of Geneva, Switzerland (CCER, N°2017–0110 and CER-amendment n°4) and Cameroonian National Ethics Committee for Human Health Research (N°2022/12/1518/CE/CNERSH/SP). NCT: 03757299.

Multi-scale deep learning ensemble for segmentation of endometriotic lesions

AbstractUltrasound is a readily available, non-invasive and low-cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, both in terms of workload reduction and in increasing the overall accuracy of this type of examination and its outcome. However, although these techniques are spreading rapidly in a variety of domains, their application to endometriosis is still very limited. To fill this gap, we propose and evaluate a novel multi-scale ensemble approach for the automatic segmentation of endometriosis lesions from transvaginal ultrasounds. The peculiarity of the method lies in its high discrimination capability, obtained by combining, in a fusion fashion, multiple Convolutional Neural Networks trained on data at different granularity. The experimental validation carried out shows that: (i) the proposed method allows to significantly improve the performance of the individual neural networks, even in the presence of a limited training set; (ii) with a Dice coefficient of 82%, it represents a valid solution to increase the diagnostic efficacy of the ultrasound examination against such a pathology.

3DFRINet: A Framework for the Detection and Diagnosis of Fracture Related Infection in Low Extremities Based on 18F-FDG PET/CT 3D Images

Fracture related infection (FRI) is one of the most devastating complications after fracture surgery in the lower extremities, which can lead to extremely high morbidity and medical costs. Therefore, early comprehensive evaluation and accurate diagnosis of patients are critical for appropriate treatment, prevention of complications, and good prognosis. 18Fluoro-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is one of the most commonly used medical imaging modalities for diagnosing FRI. With the development of deep learning, more neural networks have been proposed and become powerful computer-aided diagnosis tools in medical imaging. Therefore, a fully automated two-stage framework for FRI detection and diagnosis, 3DFRINet (Three Dimension FRI Network), is proposed for 18F-FDG PET/CT 3D imaging. The first stage can effectively extract and fuse the features of both modalities to accurately locate the lesion by the dual-branch design and attention module. The second stage reduces the dimensionality of the image by using the maximum intensity projection, which retains the effective features while reducing the computational effort and achieving excellent diagnostic performance. The diagnostic performance of lesions reached 91.55% accuracy, 0.9331 AUC, and 0.9250 F1 score. 3DFRINet has an advantage over six nuclear medicine experts in each classification metric. The statistical analysis shows that 3DFRINet is equivalent or superior to the primary nuclear medicine physicians and comparable to the senior nuclear medicine physicians. In conclusion, this study first proposed a method based on 18F-FDG PET/CT three-dimensional imaging for FRI location and diagnosis. This method shows superior lesion detection rate and diagnostic efficiency and therefore has good prospects for clinical application.

Diagnostic Utility of Artificial Intelligence–assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

Background and objectiveAccurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone. MethodsA total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration–cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2–5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test. Key findings and limitationsOverall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS– and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy. Conclusions and clinical implicationsThe tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. Patient summaryThe computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

BackgroundDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. MethodsDECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics. ResultsSegresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. ConclusionIn this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.

Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection

The increasing rates of colorectal cancer and associated mortality have attracted interest in the use of computer-aided diagnosis tools based on artificial intelligence (AI) for the detection of polyps at an early stage. Most AI models are implemented on software platforms; however, due to the demands of embedded devices, hardware implementations have to fulfil the demands of real-time applications with better accuracy and low-power consumption. In this letter, we propose an optimized four-layer network that can be implanted into an embedded device and determine the feasibility of implanting our convolutional neural network (CNN) into a microprocessor. The essential functions of the CNN (i.e., padding, convolution, ReLU, max-pooling, fully-connected, and softmax layers) are implemented in the microprocessor. The proposed method achieves efficient classification with high performance and takes only 2.5488mW at a working frequency of 8MHz. We conclude this letter with a discussion of the results and future direction of research.

A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images.

Early detection of retinal disorders using optical coherence tomography (OCT) images can prevent vision loss. Since manual screening can be time-consuming, tedious, and fallible, we present a reliable computer-aided diagnosis (CAD) software based on deep learning. Also, we made efforts to increase the interpretability of the deep learning methods, overcome their vague and black box nature, and also understand their behavior in the diagnosis. We propose a novel method to improve the interpretability of the used deep neural network by embedding the rich semantic information of abnormal areas based on the ophthalmologists' interpretations and medical descriptions in the OCT images. Finally, we trained the classification network on a small subset of the online publicly available University of California San Diego (UCSD) dataset with an overall of 29,800 OCT images. The experimental results on the 1000 test OCT images show that the proposed method achieves the overall precision, accuracy, sensitivity, and f1-score of 97.6%, 97.6%, 97.6%, and 97.59%, respectively. Also, the heat map images provide a clear region of interest which indicates that the interpretability of the proposed method is increased dramatically. The proposed software can help ophthalmologists in providing a second opinion to make a decision, and primitive automated diagnoses of retinal diseases and even it can be used as a screening tool, in eye clinics. Also, the improvement of the interpretability of the proposed method causes to increase in the model generalization, and therefore, it will work properly on a wide range of other OCT datasets.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.

The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in diagnosing and managing cardiovascular illnesses, given its 3D+Time (3D+T) sequence. The existing deep learning methods are constrained in their ability to 3D+T CMR segmentation, due to: (1) Limited motion perception. The complexity of heart beating renders the motion perception in 3D+T CMR, including the long-range and cross-slice motions. The existing methods' local perception and slice-fixed perception directly limit the performance of 3D+T CMR perception. (2) Lack of labels. Due to the expensive labeling cost of the 3D+T CMR sequence, the labels of 3D+T CMR only contain the end-diastolic and end-systolic frames. The incomplete labeling scheme causes inefficient supervision. Hence, we propose a novel spatio-temporal adaptation network with clinical prior embedding learning (STANet) to ensure efficient spatio-temporal perception and optimization on 3D+T CMR segmentation. (1) A spatio-temporal adaptive convolution (STAC) treats the 3D+T CMR sequence as a whole for perception. The long-distance motion correlation is embedded into the structural perception by learnable weight regularization to balance long-range motion perception. The structural similarity is measured by cross-attention to adaptively correlate the cross-slice motion. (2) A clinical prior embedding learning strategy (CPE) is proposed to optimize the partially labeled 3D+T CMR segmentation dynamically by embedding clinical priors into optimization. STANet achieves outstanding performance with Dice of 0.917 and 0.94 on two public datasets (ACDC and STACOM), which indicates STANet has the potential to be incorporated into computer-aided diagnosis tools for clinical application.

LAMIS-DMDB: A new full field digital mammography database for breast cancer AI-CAD researches

The current state of machine learning (ML) and deep learning (DL) shows that they are powerful tools that can be used to glean knowledge from a huge amount of data. While a great deal of research in this area has centered around improving the accuracy and efficiency of training and inference algorithms, there has been relatively little attention paid to the equally important problem of monitoring the quality of data. To tackle this challenge, we have created a cutting-edge breast database capable of employing ML and DL algorithms to detect and classify breast cancer. It is noteworthy that compared to other digital mammogram databases (DMDB), the Laboratory of Mathematics, Informatics and System (LAMIS) images have better resolution and are in full-field digital mammography (FFDM) format. Furthermore, the images can be accessed according to their abnormality (normal, benign, malignant), lesions classification (mass, calcification, architectural distortion, multiple finding), American College of Radiology (ACR) density classification (ACR1, ACR2, ACR3, ACR4) and Breast Imaging Reporting and Data System (BI-RADS). Specifically, the database is designed to provide a variety of metadata as well as clinical data in simple comma separated values (CSV) format that can be really beneficial to the breast cancer research community and to the development of computer-aided diagnosis (CADx) tools that rely on artificial intelligence (AI) algorithms, such as those based on DL models.

Severity Grading and Early Detection of Alzheimer’s Disease through Transfer Learning

Alzheimer’s disease (AD) is an illness affecting the neurological system in people commonly aged 65 years and older. It is one of the leading causes of dementia and, subsequently, the cause of death as it gradually affects and destroys brain cells. In recent years, the detection of AD has been examined in ways to mitigate its impacts while considering early detection through computer-aided diagnosis (CAD) tools. In this study, we developed deep learning models that focus on early detection and classifying each case, non-demented, moderate-demented, mild-demented, and very-mild-demented, accordingly through transfer learning (TL); an AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet by utilizing magnetic resonance images (MRI) and the use of image augmentation. The acquired images, a total of 12,800 images and four classifications, had to go through a pre-processing phase to be balanced and fit the criteria of each model. Each of these proposed models split the data into 80% training and 20% testing. AlexNet performed an average accuracy of 98.05%, GoogleNet (InceptionV3) performed an average accuracy of 97.80%, and ResNet-50 had an average performing accuracy of 91.11%. The transfer learning approach assists when there is not adequate data to train a network from the start, which aids in tackling one of the major challenges faced when working with deep learning.

Assessment of short-term effect of platelet-rich plasma treatment of tendinosis using texture analysis of ultrasound images.

Computer-aided diagnosis (i.e., texture analyses) tools are becoming increasingly beneficial methods to monitor subtle tissue changes. The aim of this pilot study was to investigate short-term effect of platelet rich plasma (PRP) treatment in supraspinatus and common extensor of the forearm tendinosis by using texture analysis of ultrasound (US) images as well as by clinical questionnaires. Thirteen patients (7 male and 6 female, age 36-60 years, mean age 51.2 ± 5.2) were followed after US guided PRP treatment for tendinosis of two tendons (9 patients with lateral epicondylitis and 4 with supraspinatus tendinosis). Clinical and US assessment was performed prior to as well as 3 months after PRP treatment with validated clinical questionnaires. Tissue response in tendons was assessed by using gray level run length matrix method (GLRLM) of US images. All patients improved of tendinosis symptoms after PRP treatment according to clinical questionnaires. Almost all GLRLM features were statistically improved 3 months after PRP treatment. GLRLM-long run high gray level emphasis (LRLGLE) revealed the best moderate positive and statistically significant correlation after PRP (r = 0.4373, p = 0.0255), followed by GLRLM-low gray level run emphasis (LGLRE) (r = 0.3877, p = 0.05). Texture analysis of tendinosis US images was a useful quantitative method for the assessment of tendon remodeling after minimally invasive PRP treatment. GLRLM features have the potential to become useful imaging biomarkers to monitor spatial and time limited tissue response after PRP, however larger studies with similar protocols are needed.

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose ‘misalignment augmentation’ as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases.

It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.

Improving model fairness in image-based computer-aided diagnosis

Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Despite this concern, the problem of improving model fairness in medical image classification by deep learning has yet to be fully studied. To address this issue, we propose an algorithm that leverages the marginal pairwise equal opportunity to reduce bias in medical image classification. Our evaluations across four tasks using four independent large-scale cohorts demonstrate that our proposed algorithm not only improves fairness in individual and intersectional subgroups but also maintains overall performance. Specifically, the relative change in pairwise fairness difference between our proposed model and the baseline model was reduced by over 35%, while the relative change in AUC value was typically within 1%. By reducing the bias generated by deep learning models, our proposed approach can potentially alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis.

AN APPROACH TO COMPUTER-AIDED DIAGNOSIS OF HEART DISORDERS USING WAVELETS AND DEEP LEARNING APPLIED TO ELECTROCARDIOGRAMS (EKGS)

Purpose: The purpose of this study was to evaluate the potential of deep learning as a tool for computer-aided diagnosis of heart disorders based on EKG signals, using wavelet transformations to generate images. The research question was whether deep learning algorithms could accurately diagnose heart disorders and provide a valuable complement to traditional EKG views. Methods: We trained five Convolutional Neural Networks (CNNs) using EKG data obtained from the Physionet public database. The algorithms were developed using MATLAB version 2018b and the toolboxes for digital signal processing, neural networks, and wavelets. We evaluated the performance of the CNNs using accuracy, sensitivity, specificity, positive predictive value, and negative predictive value as metrics. Results: The CNNs demonstrated accuracy greater than 90%, and achieved good performance for the other evaluated parameters. We also identified that the representation of EKGs as scalograms showed potential for use as a complement to traditional EKG views. Conclusion: Our findings demonstrate that deep learning is a promising tool for diagnosing heart disorders based on EKG signals, and can be a valuable complement to traditional EKG views. While automated diagnoses should not replace clinical judgment, deep learning can provide additional support to healthcare professionals. Further research should explore the potential of deep learning for medical diagnosis and the use of scalograms as a complementary tool in clinical practice.

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.

Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

3D unsupervised anomaly detection through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction for unsupervised anomaly detection (VMPR-UAD) in low-dose chest CT. Presumably, this is the novel method that only requires data from healthy patients for training to identify three-dimensional (3D) regions containing any anomalies. The method has three key components. Unlike existing computer-aided diagnosis tools that use conventional CT slices as the network input, our method (1) improves the recognition of 3D lung structures by virtually projecting an extracted 3D lung region to obtain two-dimensional (2D) images from diverse views to serve as network inputs, (2) accommodates the input diversity gain for accurate anomaly detection, and (3) achieves 3D anomaly/disease localization through a novel 3D map restoration method using multiple 2D anomaly maps. The proposed method based on unsupervised learning showed a high performance in pneumonia, tuberculosis, and both diseases with patient-level anomaly detection performance of 0.965 area under the curve (AUC) (95% confidence interval (CI); (0.955, 0.972)), 0.948 AUC (95% CI; (0.928, 0.966)), and 0.963 AUC (95% CI; (0.955, 0.970)), respectively. Additionally, our technology visualizes anomalous regions in the 3D perspective. This achieved 93% accuracy in visualizing the location of lung cancer lesions through external validation. These results highlight the potential of a new AI methodology without utilizing disease data learning; this can secure AI model prediction stability by reducing the false negative rate that occurs in various patterns of diseases.