Background/Objectives: This study aims to evaluate and compare the diagnostic accuracy of skin lesion classification among three different classifiers: AI-based image classification, an expert dermatologist, and a non-expert. Given the rising interes...
An important hurdle in medical diagnostics is the high-quality and interpretable classification of audio signals. In this study, we present an image-based representation of infant crying audio files to predict abnormal infant cries using a vision tra...
Growing amounts of municipal waste and the need for efficient recycling demand automated and accurate classification systems. This paper investigates deep learning approaches for multi-class waste sorting based on image data, comparing three widely u...
The increase in the number of nuclear power plants utilized for achieving carbon neutrality has emphasized the importance of managing high-radiation spent nuclear fuel (SNF). Traditional inspection methods, such as gamma emission tomography (GET), ha...
Artificial intelligence-driven computer vision has undergone rapid expansion in recent years, largely propelled by progress in deep learning techniques and the availability of extensive annotated datasets. Nevertheless, the large-scale adoption of su...
Neuropsychiatric disorders including attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ) exhibit significant clinical heterogeneity and overlapping symptom profiles, which severely limits the diagnostic spec...
Recent advances in vision-language models such as BLIP-2 have made AI-generated image descriptions increasingly fluent and difficult to distinguish from human-authored texts. This paper investigates whether such differences can still be reliably dete...
Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador...
This study evaluates whether a multimodal retrieval-augmented generation (RAG) pipeline originally developed for early modern woodcuts can be effectively transferred to the domain of medieval manuscript illumination. Using a dataset of Wenzelsbibel m...
Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was...
Stroke is a major global cause of death and disability and necessitates both quick diagnosis and treatment within narrow windows of opportunity. CT scanning is still the first-line imaging in the acute phase, but correct interpretation may not always...
This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses th...
Objective: This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be n...
Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing...
In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to...
Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Met...
Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in...
Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the c...
The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and...
Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with a poor prognosis. Traditional diagnosis relies on invasive biopsies, which pose surgical risks. Advances in artificial intelligence (AI) and machine learning (ML) have improv...
The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is...
In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion...
Background/Objectives: The aim of this study was to establish a histology-based gold standard for the evaluation of artificial intelligence (AI)-based caries detection systems on proximal surfaces in bitewing images. Methods: Extracted human teeth we...
Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring...
Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing i...
We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the re...
Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated p...
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with mult...
Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer...
Background: The Mayo imaging classification (MIC) for polycystic kidney disease (PKD) is a crucial basis for clinical treatment decisions; however, the volumetric assessment for its evaluation remains tedious and inaccurate. While the ellipsoid metho...
Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during develop...
Lower back pain (LBP) is a leading cause of disability globally, characterized by multifactorial origins that complicate accurate diagnosis and effective treatment planning. Artificial intelligence (AI), including machine learning (ML), deep learning...
Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in...
Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the perform...
This review consists of three parts related to the AT(N) framework, providing a comprehensive overview of biomarker-based diagnosis of Alzheimer’s disease (AD). In recent years, management of AD has shifted from a symptom-based approach toward...
Background/Objectives: This review explores the role of artificial intelligence (AI), particularly with deep learning and machine learning, in the detection and classification of papilledema using retinal fundus imaging. Methods: The study synthesize...
Background/Objective: Ultrasound is the first-line imaging modality for thyroid nodule assessment; however, it remains highly operator-dependent and subject to interobserver variability. Artificial intelligence (AI)-based systems have been proposed t...
Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for t...
Introduction: Cardiovascular risk prediction remains challenging, particularly in patients with intermediate risk, mixed dyslipidemia, elevated lipoprotein(a), or variable lipid profiles. Conventional risk calculators may not fully capture nonlinear...
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagn...
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent...
Ewing sarcoma is a highly malignant tumor whose histological appearance often overlaps with that of other undifferentiated round-cell and ovoid-cell tumors, making accurate diagnosis challenging. Selecting the most appropriate immunohistochemical and...
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Tr...
This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo...
This paper presents a novel approach for the early detection of cattle diseases. We present a uniquely integrated image classification-based project for real-time cattle disease diagnosis that combines image classification models to identify diseases...
Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician’s expertise, pose challenges. Esophageal cancer features often man...
This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection o...
Background/Objectives: Sarcomas are a rare and heterogeneous group of malignant tumors, which makes early detection and grading particularly challenging. Diagnosis traditionally relies on expert visual interpretation of histopathological biopsies and...
Objective: Although prior research developed an artificial intelligence (AI)-based classification system predicting colorectal lesion histology, the heavy computational demands limited its practical application. Recent advancements in medical AI emph...
Background/Objectives: The objective of this study was to evaluate the performance of Breast-SlimView®, a deep convolutional neural network for the automatic classification of BI-RADS and breast density in MLO (mediolateral oblique) and CC (crani...
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