Artificial Intelligence And Deep Learning In Ultrasound Diagnosis: A Comprehensive Review Of Clinical Applications, Diagnostic Accuracy, And Future Perspectives
Jul 2026· Adolescência e Saúde· Vol 21, pp. 792-799· 0 citations· 1 references
TL;DR
It is highlighted that while AI shows tremendous promise for enhancing diagnostic accuracy and clinical efficiency in ultrasound medicine, successful implementation requires multidisciplinary collaboration, robust validation frameworks, and organizational infrastructure for sustainable clinical integration.
Abstract
Artificial intelligence (AI) and deep learning (DL) technologies have revolutionized medical imaging and diagnostics. This comprehensive review synthesizes current evidence on the application, diagnostic accuracy, challenges, and future directions of AI algorithms in ultrasound (US) imaging. We conducted a systematic analysis of peer-reviewed literature examining diagnostic accuracy of deep learning in medical imaging and specific AI applications in ultrasound-guided procedures, breast US diagnosis, AI-based radiomics, and regional anesthesia guidance. Our findings demonstrate that AI algorithms achieve high diagnostic accuracy across multiple ultrasound applications, with sensitivity and specificity often comparable to or exceeding experienced radiologists. In ophthalmology imaging, AI achieved area under curve (AUC) of 0.939-0.969 for various retinal pathologies. In breast ultrasound, AI systems demonstrated sensitivity of 92.5% and accuracy of 78.6% for malignant lesion detection. Deep learning radiomics achieved AUC of 0.978 in pancreatic adenocarcinoma diagnosis and 0.97 in breast cancer characterization. However, significant challenges remain including standardization of training datasets, external validation, clinical workflow integration, regulatory compliance, and reimbursement issues. This review highlights that while AI shows tremendous promise for enhancing diagnostic accuracy and clinical efficiency in ultrasound medicine, successful implementation requires multidisciplinary collaboration, robust validation frameworks, and organizational infrastructure for sustainable clinical integration.
A comprehensive narrative review of current AI/DL applications in ultrasound diagnosis, synthesising evidence across four major clinical domains and identifying recurring limitations across the literature - dataset heterogeneity, limited external/multicentre validation, and interpretability gaps - and outlines directions for future research toward clinically deployable, trustworthy AI-assisted ultrasound diagnosis.
R. Sivakumar, Arati Shahapurkar, J. G et al.· Adolescência e Saúde· 0 citations
The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care in the next generation of diagnostic imaging.
Mr. Vishal Walia, Mr. Honey Thakur, Ms. Ashwarya Sharma et al.· PAIN, JOINTS, SPINE· 0 citations
This comprehensive review systematically examines the architecture, functionality, and clinical effectiveness of ANN-based models applied to a diverse spectrum of medical imaging modalities, encompassing mammography, magnetic resonance imaging (MRI), computed tomography (CT), fundus photography, and dermoscopy.
Maryam Omar Al-Tohamy, Abdel Hamid, A. Arjiah et al.· Al-Farooq Journal of Science...· 0 citations
Background of the Study:Brain tumors are among the most serious neurological disorders affecting both adults and children worldwide. Early detection and accurate diagnosis are essential for timely treatment planning, improved prognosis, and reduction in morbidity and mortality. Magnetic Resonance Imaging (MRI) is considered the gold standard imaging modality for brain tumor evaluation because of its superior soft tissue contrast, multiplanar imaging capability, and absence of ionizing radiation. Conventional MRI interpretation, however, depends heavily on radiologist expertise and may sometimes result in delayed diagnosis, interobserver variability, and difficulty in detecting subtle lesions during early stages. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) algorithms, have shown promising applications in medical imaging. AI-based computer-aided diagnostic systems can analyze large volumes of MRI data rapidly and assist radiologists in identifying abnormal brain tissue with high accuracy. Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, segmentation, tumor localization, and differentiation between benign and malignant lesions. AI systems are increasingly being integrated into neuroradiology workflows to improve diagnostic efficiency and reduce human error. Several retrospective studies have reported that AI-assisted MRI analysis enhances sensitivity and specificity in detecting brain tumors, especially gliomas, meningiomas, pituitary adenomas, and metastatic lesions. AI algorithms can also help identify tumor boundaries, edema, necrosis, and tissue heterogeneity that may not be easily distinguishable on routine imaging. Furthermore, AI-based systems support early-stage detection, treatment planning, prognosis prediction, and follow-up assessment. Despite significant progress, challenges such as dataset variability, algorithm bias, lack of standardization, and limited clinical validation still exist. Therefore, the present retrospective diagnostic accuracy study was conducted to evaluate the role of artificial intelligence in the early detection of brain tumors using MRI and to assess its diagnostic performance compared with conventional radiological interpretation.Aim of the Study:To evaluate the effectiveness and diagnostic accuracy of Artificial Intelligence in the early detection of brain tumors using MRI imaging.Objectives of the Study:
To assess the diagnostic accuracy of AI-based MRI analysis in detecting brain tumors.
To compare AI-assisted diagnosis with conventional radiologist interpretation.
To evaluate the sensitivity and specificity of AI algorithms in early tumor detection.
To analyze the role of AI in tumor segmentation and classification.
To identify the advantages and limitations of AI applications in neuroimaging.
To evaluate the future scope of AI in brain tumor diagnosis and clinical decision-making.
Methodology: The present study was conducted as a retrospective diagnostic accuracy study using previously acquired MRI brain images from a tertiary care hospital radiology database. MRI scans performed between 2020 and 2025 were reviewed for analysis.
A total of 120 MRI brain cases were included in the study, consisting of:
80 confirmed brain tumor cases
40 normal/control MRI scans
The study included patients diagnosed with various brain tumors such as:
Glioma
Meningioma
Pituitary adenoma
Metastatic brain tumors
MRI sequences analyzed included:
T1-weighted imaging
T2-weighted imaging
FLAIR imaging
Diffusion-weighted imaging (DWI)
Contrast-enhanced MRI sequences
AI-based image analysis was performed using deep learning algorithms, primarily Convolutional Neural Network (CNN)-based models trained for brain tumor detection and classification. The AI results were compared with radiologist reports and histopathological findings, which were considered the reference standard.
Data collected included:
Tumor detection rate
Sensitivity
Specificity
Accuracy
Positive Predictive Value (PPV)
Negative Predictive Value (NPV)
Statistical analysis was performed using SPSS software. Diagnostic accuracy parameters were calculated, and Receiver Operating Characteristic (ROC) curve analysis was used to evaluate AI performance.
Results: The findings of the study demonstrated that Artificial Intelligence showed high diagnostic accuracy in detecting brain tumors using MRI imaging. Out of 80 confirmed tumor cases, the AI system correctly identified 74 cases, while 6 cases were missed. Among the 40 normal MRI scans, 36 cases were correctly classified as non-tumorous. AI-assisted MRI interpretation demonstrated faster lesion detection and improved visualization of tumor margins compared to routine manual interpretation. CNN-based algorithms showed excellent performance in identifying gliomas and metastatic lesions.
The study also found that AI significantly improved:Early-stage lesion identificationTumor segmentation accuracyDetection of small intracranial abnormalitiesWorkflow efficiency in radiology departmentsROC curve analysis demonstrated excellent diagnostic performance with an Area Under Curve (AUC) value of 0.93.However, certain limitations were observed:Reduced performance in very small lesionsOccasional false-positive findings in inflammatory lesionsDependence on high-quality MRI datasetsNeed for large annotated datasets for algorithm trainingConclusion:The present retrospective study demonstrated that Artificial Intelligence is a highly effective tool for early detection of brain tumors using MRI imaging. AI-based diagnostic systems showed high sensitivity, specificity, and overall diagnostic accuracy in identifying intracranial tumors. The integration of AI with MRI can significantly enhance radiological interpretation, reduce diagnostic delays, and improve clinical decision-making.AI-assisted imaging also supports accurate tumor segmentation, classification, and early detection of subtle abnormalities, thereby improving patient management and treatment planning. Although AI cannot completely replace radiologists, it can serve as an important supportive tool in neuroradiology practice.Further multicenter studies with larger datasets and standardized AI models are recommended to improve reliability, clinical applicability, and integration of AI technologies into routine neuroimaging workflows.Summary: This retrospective diagnostic accuracy study evaluated the role of Artificial Intelligence in the early detection of brain tumors using MRI. The findings revealed that AI-based MRI analysis provides high sensitivity, specificity, and diagnostic accuracy in detecting brain tumors. AI-assisted systems improved lesion detection, tumor segmentation, and radiology workflow efficiency. The study highlights the growing importance of AI in modern neuroradiology and supports its future integration into clinical imaging practice.
D. Kumar, Supervisor Verma, Ms. Sufia et al.· PAIN, JOINTS, SPINE· 0 citations
This review summarizes AI applications in thyroid ultrasound, including image preprocessing, nodule segmentation, quantitative feature analysis, benign-malignant differentiation, TIRADS optimization and automated reporting, and highlights AI’s potential in enhancing diagnostic consistency and accuracy.
Ye Guo, Tong Zhao, Li-li Zhang et al.· Frontiers in Endocrinology· 0 citations
AI should be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.
Bayan Alghamdi, Eman M Alrewily, Sharefa S Alghamdi· Ultrasound Quarterly· 0 citations
