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Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue. OBJECTIVE: We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelin…

A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial Intelligence.

AIM: The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as SVM (Support Vec … mated processing of CXR images which are reasonable and frequently used for TB diagnosis, employing Artificial Intelligence (AI) techniques provides novel possibilities. OBJECTIVE: The objective of the study was to identify respiratory disorders, radiologists devote a lot of time reviewing each of the CXR images. As such, they can identify the type of disease using auto …

Artificial intelligence-based model for diagnosing Helicobacter pylori in whole-slide images.

INTRODUCTION: Helicobacter pylori (H. pylori) infection is considered to be a primary causative factor for gastric cancer and a common cause of chronic gastritis worldwide. Identifying H. pylori infection through hematoxylin and eosin (H&E) staining is demanding and tedious for pathologists. We aimed to use artificial intelligence (AI) models to improve the accuracy and efficiency of H. pylori diagnosis and to reduce the workload of pathologists. METHODS: Here, we developed three multi-instance learning (MIL) models: AB-MIL, DS-MIL, and Trans-MIL, to automatically detect H. pylori infection. …

Искусственный интеллект в предимптомных неврологических заболеваниях: преодоление обычных вариаций и продромальных сигнатур.

Пресимптоматические неврологические заболевания характеризуются ранними патологическими изменениями, которые происходят до появления явных клинических симптомов. Эти стадии, которые включают в себя такие продромы, как расстройство поведения сна РЕМ в Паркинсоне или слабое когнитивное расстройство в Альцгеймере, открывают важные возможности для раннего вмешательства. Однако их обнаружение по-прежнему сопряжено с трудностями из-за тонкости изменений и дублирования с обычной межиндивидуальной изменчивостью. Искусственный интеллект (AI), особенно машинное обучение (ML) и глубокое обучение (DL), пр

Совпадение в аннотации оригинала
Presymptomatic neurological diseases are marked by early pathological changes that occur before overt clinical symptoms. These stages, which … challenging due to the subtlety of changes and the overlap with normal interindividual variability. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), offers new tools to uncover hidden signatures in complex biomedical data. First, we explore how supervised ML models can detect known prodromal patterns across diverse modalities, includ …

Long-Term Gait-Balance Monitoring Artificial Intelligence System for Various Terrain Types.

A long-term gait-balance monitoring system for various terrain types was developed using an inertial measurement unit (IMU) and deep-learning model. The system aims to identify unstable gait caused by lower-limb degeneration to prevent fall-related injuries. Unlike previous studies that have only focused on gait stability in flat terrain walking, the proposed system is also capable of analyzing stability on stairs and slopes. A lightweight, nine-axis IMU was used for data collection, and a combined convolutional neural network with gated recurrent unit model was implemented on the portable Ras

Artificial intelligence enabled smart mask for speech recognition for future hearing devices.

In recent years, Lip-reading has emerged as a significant research challenge. The aim is to recognise speech by analysing Lip movements. The majority of Lip-reading technologies are based on cameras and wearable devices. However, these technologies have well-known occlusion and ambient lighting limitations, privacy concerns as well as wearable device discomfort for subjects and disturb their daily routines. Furthermore, in the era of coronavirus (COVID-19), where face masks are the norm, vision-based and wearable-based technologies for hearing aids are ineffective. To address the fundamental l

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detecti…

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.

BACKGROUND: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural n…

Dental implant planning using artificial intelligence: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: Data on the role of artificial intelligence (AI) in dental implant planning is insufficient. PURPOSE: The purpose of this systematic review with meta-analysis was to analyze and evaluate articles that assess the effectiveness of AI algorithms in dental implant planning, specifically in detecting edentulous areas and evaluating bone dimensions. MATERIAL AND METHODS: A systematic review was conducted across the MEDLINE/PubMed, Web of Science, Cochrane, and Scopus databases. In addition, a manual search was performed. The inclusion criteria consisted of peer-reviewed studi…

Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images.

BACKGROUND/OBJECTIVES: Cystoscopy is necessary for diagnosing bladder cancer, but it has limitations in identifying ambiguous lesions, such as carcinoma in situ (CIS), which leads to a high recurrence rate of bladder cancer. With the significant advancements in deep learning in the medical field, several studies have explored its application in cystoscopy. This study aimed to utilize the VGG19 and Deeplab v3+ deep learning models to classify and segment cystoscope images, respectively. METHODS: We classified cystoscope images obtained from 772 patients based on morphology (normal, papillary,

Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lea … le pancreas was 0.811 and that for the necrotic pancreatic tissue was 0.761. The performance of the artificial intelligence model and clinicians were compared. The accuracy, macro recall, and macro specificity of the improved V-net were 0.854, 0.850 and 0.923, respectively, which were all significantly higher than those of the senior and junior clinicians (P<0.05). CONC …

Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review.

Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across d…

Early detection of bloodstream infection in critically ill children using artificial intelligence.

BACKGROUND: Despite the high mortality associated with bloodstream infection (BSI), early detection of this condition is challenging in critical settings. The objective of this study was to create a machine learning tool for rapid recognition of BSI in critically ill children. METHODS: Data were extracted from a derivative cohort comprising patients who underwent at least one blood culture during hospitalization in the pediatric intensive care unit (PICU) of a tertiary hospital from January 2020 to June 2023 for model development. Data from another tertiary hospital were utilized for external

Artificial intelligence and explanation: How, why, and when to explain black boxes.

Artificial intelligence (AI) is infiltrating nearly all fields of science by storm. One notorious property that AI algorithms bring is their so-called black box character. In particular, they are said to be inherently unexplainable algorithms. Of course, such characteristics would pose a problem for the medical world, including radiology. The patient journey is filled with explanations along the way, from diagnoses to treatment, follow-up, and more. If we were to replace part of these steps with non-explanatory algorithms, we could lose grip on vital aspects such as finding mistakes, patient t…

Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.

Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms hav…

[Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization].

BACKGROUND: Artificial intelligence (AI) has the potential to fundamentally change radiology workflow. OBJECTIVES: This review article provides an overview of AI applications in cardiovascular radiology with a focus on image acquisition, image reconstruction, and workflow optimization. MATERIALS AND METHODS: First, established applications of AI are presented for cardiovascular computed tomography (CT) and magnetic resonance imaging (MRI). Building on this, we describe the range of applications that are currently being developed and evaluated. The practical benefits, opportunities, and poten…

Artificial Intelligence-based Assessment of Facial Symmetry Aesthetics of Saudi Arabian Population.

The purpose of this study is to investigate facial symmetry aesthetics (FSA) in the Saudi Arabian population using artificial intelligence (AI).Two hundred and ten people from a range of demographic backgrounds participated in an observational cross-sectional study that was done at a hospital. Standardized posed photos of the face and smile were taken using a Canon camera utilizing a stratified random sample approach. Webceph software (Korea) with AI was used to evaluate macro, micro, and tiny aesthetic factors. The data were analyzed using paired t-tests, post hoc Bonferroni testing, analysis…

Artificial intelligence in colorectal multidisciplinary team meetings. What are the medicolegal implications?

AIM: To give an insight into areas for future development and suggestions in the complexities of incorporation of AI into human colorectal cancer (CRC) care while bringing into focus the importance of clinicians' roles in patient care. METHODS: Existing literature around AI use in CRC care is reviewed and potential regulatory issues and medicolegal implications around its implementation in CRC multidisciplinary team meetings (MDTs) are identified. RESULTS: Challenges with patient privacy and confidentiality, patient consent, inequity and bias, patient autonomy, as well as AI system transpare

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods h…

Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

BACKGROUND: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital. This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal a…