Diagnostic Accuracy Research Articles

Classification of Brain Infarction using Deep Learning techniques on Magnetic Resonance Imaging (MRI)

Background: This research concentrated on employing Convolutional Neural Networks (CNNs) to classify brain infarctions. A stroke occurs due to a blockage or hemorrhage in the blood vessels, which disrupts or diminishes the blood supply to the brain, leading to the death of brain cells. With the progress in machine learning techniques in medical imaging, early detection of stroke has become increasingly feasible, playing a crucial role in the diagnosis and treatment of this potentially fatal condition. Objective: Building an intelligent CNN model to classify types of brain infarction using up algorithms Median Filter preserving edges and efficient minimization of noise with durability against flowing noise, Fuzzy C – Mean to Image Segmentation and Recognition of Region of Interest for infarction location and size of the infarction for brain infarction Identification, GLCM, and FOSF. Methodology: The research was carried out in a private hospital; in Baghdad, Iraq, on 60 patients with brain infarction. Included were 9 hyperacute (>6 hours post brain infarction), 19 acute (7-72 hours), 12 subacute (4-7 days), and 20 chronic (< 15 days). We have introduced a Convolutional Neural Network (CNN) model as a solution to predict the likelihood of a patient experiencing a stroke at an early stage, aiming for maximize effectiveness and precision. Results: The ROC curve demonstrates that the average apparent diffusion coefficient (aveg (ADC) mm²/s) and relative apparent diffusion coefficient (rADC%) are two reliable and effective measures for monitoring brain infarction developments. High sensitivity indicates a strong ability to detect true positives (cases of infarction), while the shape and position of the curve suggest that average (ADC) mm²/s also maintains a reasonable specificity, minimizing false positives. This balance results in high overall diagnostic accuracy. Discussion: Both rACD (%) and aveg (ADC) mm²/s are robust indicators for the imaging monitoring of brain infarction developments. The inclusion of AI in the assessment process may result marginal improvements in specificity and accuracy.

Point-of-Care Diagnostics (POCD) in Resource-Limited Settings

Diagnostic testing is critical to provide adequate healthcare, and the emergence of various rapid point-of-care diagnostics (POCD) allows for greater access and broader implementation in resource-limited settings [...]

The Moderating Effect of Stakeholders Management on the Relationship Between Organizational Characteristics and Performance of African Ports

The main objective of this study was to determine the moderating effect of stakeholders’ management on the relationship between organizational characteristics and the performance of seaports in Anglophone Africa. This study adopted a positivist research philosophy with a descriptive cross-sectional census survey design. Structured questionnaires were employed to collect primary data targeting executive managers of container-handling seaport terminals in Africa who are knowledgeable in port operations and management. Additional published data was also obtained from the websites of some of the ports. The response rate was 83.6%. The reliability and validity of the indicator items were ascertained through diagnostic tests. The use of SRMR and NFI confirmed model fitness. Partial Least Squares Structural Equation Modeling using Smartpls 4.0 software was used for data analysis and measurement model estimation to test the null hypothesis that there is no significant moderating effect of stakeholders’ management on the relationship between organizational characteristics and the performance of seaports in Africa. The finding established a positive and significant moderating effect of stakeholders’ management on the relationship. The study concluded that sound and competent stakeholders’ management enhances port container terminal performance thereby creating a competitive advantage for ports in Anglophone Africa. The study adds to new knowledge, theory, policy, and practice by recommending that seaports in Africa should mitigate possible conflicts from stakeholders while developing new ports or expanding existing ports by embracing stakeholder theory.

Integrating imprecise data in generative models using interval-valued Variational Autoencoders

Variational Autoencoders (VAEs) enable the integration of diverse data sources into a unified latent representation, facilitating the fusion of information from various inputs and the creation of disentangled representations that separate different factors of variation in the data. Traditional VAEs, however, are limited by assuming a single prior distribution for latent variables, which restricts their ability to handle epistemic uncertainty from imprecise measurements and incomplete data. This paper introduces the Interval-Valued Variational Autoencoder (iVAE), which employs a family of prior distributions and incorporates specialized neurons and redefined objective functions for handling interval-valued data. This architecture maintains computational efficiency while extending the model’s applicability to scenarios with pronounced epistemic uncertainty. The iVAE’s efficacy is demonstrated in managing two types of data: intrinsically interval-valued and noisy data preprocessed into interval formats. The first category is exemplified by a graphical analysis of questionnaires, while the second involves case studies focused on estimating the remaining useful life of aviation engines, where the iVAE outperforms traditional methods, thereby providing more accurate diagnostics and robust predictions.

A Novel High-Throughput and Sensitive Electrochemiluminescence Immunoassay System

(1) Background: In vitro diagnostic (IVD) tests are the main means of obtaining diagnostic information for clinical purposes. The electrochemiluminescence immunoassay (ECLIA) has become an important in vitro diagnostic technique. It has unique advantages and broad market prospects due to its sensitivity, detection limit, detection range and reagent stability. At present, there is a need to develop and optimize electrochemiluminescence immunoassay subsystems to achieve high-throughput outputs and accurate detection of instruments to promote their clinical application. (2) Methods: On the basis of the demand for clinical testing instruments with high detection accuracy and speed, this study constructed an electrochemiluminescence immunoassay system by designing magnetic separation modules, introducing PMT and optimizing the system timing regulation capability. (3) Results: The magnetic separation modules can increase the detection accuracy, the PMT system increases the detection sensitivity and optimized system timing can achieve maximum test output. Furthermore, this system performs well in terms of its linearity, detection limit, signal-to-noise ratio, precision and accuracy. (4) Conclusions: The electrochemiluminescence immunoassay system is capable of high throughput with good sensitivity and accuracy, meeting the basic requirements of clinical applications for detection capacity and output throughput.

Evaluating the Diagnostic Accuracy of C-Reactive Protein in Diagnosing Pneumonia in Children Using Blood Culture as the Gold Standard

Evaluating the Diagnostic Accuracy of C-Reactive Protein in Diagnosing Pneumonia in Children Using Blood Culture as the Gold Standard

Fine needle aspiration cytology of Basal Cell Carcinoma: A Case series

Basal cell carcinoma (BCC) is the most common type of skin cancer, characterized by its local invasiveness and low metastatic potential. Fine needle aspiration (FNA) is a minimally invasive diagnostic technique that can be used to obtain cytological samples from suspected BCC lesions. This study aims to evaluate the efficacy and diagnostic accuracy of FNA in the diagnosis of BCC of the skin.

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic examination of tissue slices, providing valuable insights into cellular structures and pathological features. On the other hand, genomic data provides information about tumor gene expression and functionality. The fusion of these two distinct data types is crucial for gaining a more comprehensive understanding of tumor characteristics and progression. In the past, many studies relied on single-modal approaches for tumor diagnosis. However, these approaches had limitations as they were unable to fully harness the information from multiple data sources. To address these limitations, researchers have turned to multi-modal methods that concurrently leverage both histopathological images and genomic data. These methods better capture the multifaceted nature of tumors and enhance diagnostic accuracy. Nonetheless, existing multi-modal methods have, to some extent, oversimplified the extraction processes for both modalities and the fusion process. In this study, we presented a dual-branch neural network, namely SG-Fusion. Specifically, for the histopathological modality, we utilize the Swin-Transformer structure to capture both local and global features and incorporate contrastive learning to encourage the model to discern commonalities and differences in the representation space. For the genomic modality, we developed a graph convolutional network based on gene functional and expression level similarities. Additionally, our model integrates a cross-attention module to enhance information interaction and employs divergence-based regularization to enhance the model’s generalization performance. Validation conducted on glioma datasets from the Cancer Genome Atlas unequivocally demonstrates that our SG-Fusion model outperforms both single-modal methods and existing multi-modal approaches in both survival analysis and tumor grading.

Establishing the cutoff value of near visual acuity for assessment of early presbyopia.

There is limited evidence to evaluate the numerical cutoff point for detecting early presbyopia. Thus, we aimed to establish a clinically relevant optimal cutoff value of near visual acuity for detecting early presbyopia. Prospective diagnostic accuracy study. We included consecutive individuals aged ≥ 20 years with a binocular-corrected distance visual acuity of ≥ 20/25 who did not undergo ophthalmic surgery between December 17, 2020 and December 19, 2021, at two healthcare facilities in Japan. Binocular distance-corrected near visual acuity at 40cm, accommodative amplitude, awareness of presbyopia, and Near Activity Visual Questionnaire scores were examined. The optimal cutoff values of distance-corrected near visual acuity for diagnosing early presbyopia were evaluated using receiver operating characteristic plots. Among 115 participants, 74 (64.3%) had presbyopia. The proportion of participants with no difficulty performing near-vision tasks decreased markedly when near visual acuity decreased to 20/20 (> 0.00 logMAR). A cutoff value of 0.00 logMAR for distance-corrected near visual acuity was optimal, showing high sensitivity of 56.76% and specificity of 92.68%, as opposed to the commonly used cutoff value of 0.40 logMAR (20/50; sensitivity, 9.46% and specificity, 100%) for diagnosing early presbyopia. Near visual acuity of 0.00 logMAR (20/20) could be the optimal cutoff value for diagnosing early presbyopia.

Pheochromocytoma - diagnosis and treatment, review of literature

Introduction: Pheochromocytomas are rare tumors originating from the adrenal medulla, which can occur sporadically or as part of hereditary syndromes. Many patients with chromaffin tumors harbor genetic mutations, typically inherited in an autosomal dominant manner, hence genetic testing is recommended for all patients. Symptoms can result from both excessive catecholamine production and the mass effect of the tumor. Diagnosis is confirmed by elevated levels of metanephrines or normetanephrines in the plasma or urine. Radiological imaging aids in tumor localization and assessment of potential local invasion or metastasis. Preoperative preparation of all patients involves the use of α-receptor blockers and/or other medications to control arterial hypertension, arrhythmias, and fluid volume. Surgery remains the treatment of choice, with lifelong follow-up recommended.Objective:The review article aims to provide an overview of chromaffin tumor pathology, their etiology, discuss diagnostic possibilities, and indicate therapeutic options for patients.Materials and Methods:A summary of reports available in medical publications and scientific studies found in databases such as PubMed, CrossRef, Google Scholar, as well as relevant textbooks.Results:Chromaffin tumors are rare but dangerous for patients; adequate pharmacological preparation for surgery, which is usually necessary, is extremely important.Conclusions:The main role is to establish the correct diagnosis, or even suggest and conduct diagnostic tests towards a chromaffin tumor. Patient management involves controlling blood pressure, appropriate premedication, and surgical intervention. After such treatment, patients require continuous monitoring to detect any potential disease recurrence.

Cultural Factors Influencing Substance Abuse among Undergraduates of Tai Solarin University of Education, Nigeria

Over the years, substance abuse has been a major challenge in Tai Solarin University of Education, Ijagun, Ogun State, Nigeria. Some of the reasons for substance abuse among undergraduates include family upbringing, ceremonial exposure, insecurity, anticipation, sex drive and festival usage. Substance abuse among undergraduates caused property damage within educational institution and diminishes the prospects of academic success of undergraduates. While examining the cultural factors that influence substance abuse among undergraduates of Tai Solarin University of Education in Ogun State, a descriptive survey research design was employed. The study utilized a simple random sampling technique to select a sample of eight hundred (800) undergraduates. The researcher used statistical tools such as frequency counts, percentages, and chi-square tests at a significance level of 0.05. The analysis resulted in the rejection of all three hypotheses. The study identified festival, ceremony and religion as significant contributors to substance abuse among Tai Solarin University of Education undergraduates. The study recommends mandatory medical tests for substance abuse at all undergraduate levels and advocates active involvement of health educators in educating students.

Diagnostic performance of fine-needle aspiration in soft tissue tumors: Application of the World Health Organization System for Reporting Soft Tissue Cytopathology and risk of malignancy assessment.

Recently, a new World Health Organization Reporting System for Soft Tissue Cytopathology (WHO System) was introduced. To analyze the value of this system, routine fine-needle aspiration soft tissue tumor (STT) cases were reviewed. Cytology samples of STTs collected between 1954 and 2022 at the Institut Curie were used (2214 cases, including 1376 primary tumors). All specimens were classified according to the predominant cytomorphological pattern and the WHO System. The diagnostic accuracy and risk of malignancy (ROM) in each category were calculated. Final diagnoses revealed 1236 malignancies and 978 benign or low-risk tumors. The original cytological evaluation led to 21 false-negative results (0.85%) and 29 false-positive results (1.17%). Sensitivity, specificity, positive predictive value, and negative predictive value were 98.3%, 92.1%, 97.5%, and 94.2%, respectively. Overall diagnostic accuracy was 94.2%. The ROM calculated according to the WHO System was 29.87%, 2.49%, 39.62%, 51.43%, 68.42%, and 97.69% in the nondiagnostic, benign, atypical, soft tissue neoplasm of uncertain malignant potential, suspicious for malignancy, and malignant categories, respectively; however, it varied broadly depending on the morphological pattern (62.78% in spindle cell tumors, 84.58% in myxoid tumors, 3.00% in lipomatous tumors, 78.15% in epithelioid tumors, 94.26% in pleomorphic tumors, and 100% in round cell tumors). Cytology of STTs is a powerful diagnostic method. Some cytological patterns overlap in different morphological groups, and the possibility of false-negative and false-positive diagnoses may persist. This analysis evidenced utility of the WHO System, especially when combined with morphological pattern assessment. Subclassification in particular diagnostic categories allowed for calculation of the ROM, which is crucial for optimal patient management.

Linguistic and cultural validation of symptom questionnaire for visual dysfunctions (SQVD) for psychometric analysis in the Chinese context.

The Spanish scale symptom questionnaire for visual dysfunctions (SQVD) was sinicized and tested for reliability and validity in the Chinese context, employing both classical measurement theory and item response theory. A meticulous translation was conducted using the modified Brislin translation model, with input from experts for cross-cultural debugging and in-depth review. Following a pre-survey study, the Chinese version of the SQVD was finalized. A convenience sampling method was used to select 270 patients from the target group and 252 valid questionnaires were successfully collected. The Rasch model was employed to assess response category functionality, fit statistics, unidimensionality, person and item reliability, separation, targeting, and differential item functioning. Classical test theory was applied to evaluate internal consistency and retest reliability, supplemented by correlation analysis. Job characteristic curves were also plotted to assess diagnostic accuracy. The Chinese SQVD conformed to a unidimensional structure with excellent reliability and validity. Person and item reliabilities were 0.85 and 0.99, respectively, indicating, high stability. Person and item separation indices were 2.37 and 11.54, respectively, signifying strong differentiation ability. Retest reliability was 0.917, further emphasizing the stability of the scale. The area under the receiver operating characteristic curve was 0.908 (95% CI: 0.854, 0.962), with a cutoff value of 7.5 and Youden index of 0.733, highlighting the scale's high diagnostic accuracy. The translated and culturally adapted Chinese SQVD demonstrated excellent psychometric properties. With streamlined items, short assessment time, and high efficiency, the scale is a stable and reliable clinical tool for detecting a variety of conditions related to refractive, regulatory, and binocular vision dysfunction.

Generative AI in Critical Care Nephrology: Applications and Future Prospects.

Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.

Mycobacterium tuberculosis Detection Using CRISPR Technology: An Updated Systematic Review and Meta-analysis.

Rapid and precise detection of Mycobacterium tuberculosis (MTB) is paramount for effective management and control of tuberculosis. Clustered regularly interspaced short palindromic repeats (CRISPR) technology has emerged as a promising tool for pathogenic diagnosis owing to its specificity and adaptability. This systematic review and meta-analysis aimed to appraise the diagnostic accuracy of CRISPR-based techniques in identifying MTB. A meticulous search was conducted in Medline, Scopus, Embase, and ISI Web of Science to retrieve relevant studies, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality was assessed using the Joanna Briggs Institute checklist. Data synthesis and analyses, including subgroup analyses, were performed with R v 4.3.1, examining variables like CRISPR variants, gene targets, pre-amplification techniques, and signal readout methods. From 389 identified studies, 14 met the inclusion criteria, encompassing 2175 MTB strains. The pooled sensitivity and specificity of CRISPR-based techniques were 0.93 (95% CI 0.85-0.99) and 0.97 (95% CI 0.94-0.99), respectively. The pooled diagnostic odds ratio was 273.4379 (95% CI 103.3311-723.5794), with an area under the curve of 0.97 for the summary receiver operating characteristic (SROC) curve, denoting excellent diagnostic accuracy. Subgroup analyses illustrated variations in diagnostic metrics based on factors like CRISPR variant utilized, target gene, and pre-amplification methods. For instance, CRISPR-Cas12 exhibited a sensitivity and specificity of 0.93 (95% CI 0.78-0.98) and 0.98 (95% CI 0.93-1), respectively. Moreover, this technology showed a sensitivity of 96% and specificity of 100% in detecting resistant MTB. CRISPR-based methods exhibit substantial diagnostic sensitivity and specificity for detecting MTB, with notable variances across different CRISPR variants and methodological approaches. Further studies must be conducted to optimize CRISPR's potential as a diagnostic tool for MTB in a variety of clinical and research settings.

Diagnostic Accuracy of C-Reactive Protein in Acute Appendicitis

Objective: To determine diagnostic accuracy of C-reactive protein in acute appendicitis. Study Design: Cross-sectional study. Place and Duration of Study: Surgical unit Combined Military Hospital Gujranwala Pakistan, from Apr 2019 to Jan 2021. Methodology: One hundred and twenty patients were included in the study following the inclusion criteria after their written permission and willingness. C-reactive protein levels were sent pre-operatively to the Pathology department of Combined Military Hospital Gujranwala. Patients were operated on by two classified surgeons. After open and laparoscopic appendectomies, specimens were sent to Histopathologists at Armed Forces Institute of Pathology Rawalpindi. Results were compared to determine the diagnostic accuracy of C-reactive protein. Results: Out of 120 patients, 72(60%) were males and 48(40%) were females. The diagnostic accuracy of C-reactive protein in acute appendicitis was calculated by keeping tissue diagnosis histopathology as the gold standard, where 82(68.33%) were true positive, 7(5.84%) were false positive, 13(10.83%) were true negative, and 18(15%) were false negative. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were 89.13%, 67.86%, 90.11%, 65.52% and 84.17% respectively. Conclusion: The of C-reactive protein level test for the diagnosis of acute appendicitis was very sensitive but not very specific. C-reactive protein levels should be measured routinely in suspected cases of acute appendicitis, along with Total Leucocyte Count and ultrasound abdomen, as a useful marker for early diagnosis, thus reducing negative appendectomy rates.

Enhancing the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging.

Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model's performance was evaluated using metrics such as accuracy, precision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC). The interpretability of the models' decisions was enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. Results The ResNeXt-50 and Inception-v4 models demonstrated superior performance, achieving the highest accuracy and F1 scores among the tested models. Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.

Handheld Ultrasound Devices Used by Newly Certified Operators for Pneumonia in the Emergency Department—A Diagnostic Accuracy Study

The diagnostic accuracy of handheld ultrasound (HHUS) devices operated by newly certified operators for pneumonia is unknown. This multicenter diagnostic accuracy study included patients prospectively suspected of pneumonia from February 2021 to February 2022 in four emergency departments. The index test was a 14-zone focused lung ultrasound (FLUS) examination, with consolidation with air bronchograms as diagnostic criteria for pneumonia. FLUS examinations were performed by newly certified operators using HHUS. The reference standard was computed tomography (CT) and expert diagnosis using all medical records. The sensitivity and specificity of FLUS and chest X-ray (CXR) were compared using McNemar’s test. Of the 324 scanned patients, 212 (65%) had pneumonia, according to the expert diagnosis. FLUS had a sensitivity of 31% (95% CI 26–36) and a specificity of 82% (95% CI 78–86) compared with the experts’ diagnosis. Compared with CT, FLUS had a sensitivity of 32% (95% CI 27–37) and specificity of 81% (95% CI 77–85). CXR had a sensitivity of 66% (95% CI 61–72) and a specificity of 76% (95% CI 71–81) compared with the experts’ diagnosis. Compared with CT, CXR had a sensitivity of 69% (95% CI 63–74) and a specificity of 68% (95% CI 62–72). Compared with the experts’ diagnosis and CT diagnosis, FLUS performed by newly certified operators using HHUS devices had a significantly lower sensitivity for pneumonia when compared to CXR (p < 0.001). FLUS had a significantly higher specificity than CXR using CT diagnosis as a reference standard (p = 0.02). HHUS exhibited low sensitivity for pneumonia when used by newly certified operators.

Assessing the diagnostic accuracy of postnatal clinical scoring methods and foot length measurement for estimating gestational age and birthweight of newborns in low- and middle-income countries: a systematic review and meta-analysis

BackgroundThis study aimed to update systematic reviews and meta-analyses on the diagnostic accuracy of postnatal clinical scoring (PCS) methods and foot length (FL) measurement for assessing gestational age (GA) and...

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.

Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.