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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

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Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification

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Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform

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Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks

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Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application

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Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning

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Improvement in the Convolutional Neural Network for Computed Tomography Images

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Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach

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A deep learning model for prognosis prediction after intracranial hemorrhage

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Machine learning and deep learning in medicine and neuroimaging

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Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE‐artificial intelligence project

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Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda

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Improving Representation of High-Frequency Components for Medical Visual Foundation Models

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Cultural Heritage 3D Reconstruction with Diffusion Networks

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Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm

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Deep learning for cranioplasty in clinical practice: Going from synthetic to real patient data

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Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study

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Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel

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A Fully Automatic Evaluation Model of Difficult Airway Based on Semi-Supervised Deep Learning with a Few Labeled Samples

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An Attention-Based ResNet Architecture for Acute Hemorrhage Detection and Classification: Toward a Health 4.0 Digital Twin Study

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AI-Assisted Treatment Planning for Dental Implant Placement: Clinical vs AI-Generated Plans

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Concept-based Explanation for Fine-grained Images and Its Application in Infectious Keratitis Classification

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How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

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Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images

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Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

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Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Lin, E., & Yuh, E. L. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.791816 · Cited by 28

Exploring Medical Students' and Faculty's Perception on Artificial Intelligence and Robotics. A Questionnaire Survey

Sassis, L., Kefala-Karli, P., Sassi, M., & Zervides, C. (2021) · Journal of Artificial Intelligence for Medical Sciences · doi:10.2991/jaims.d.210617.002 · Cited by 26

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review

Uparela-Reyes, M. J., Villegas-Trujillo, L. M., Cespedes, J., Velásquez-Vera, M., & Rubiano, A. M. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.05.065 · Cited by 26

Neural network-based method for diagnosis and severity assessment of Graves’ orbitopathy using orbital computed tomography

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Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

Ramaekers, M., Viviers, C. G. A., Janssen, B. V., Hellström, T. A. E., Ewals, L. J. S., van der Wulp, K., Nederend, J., Jacobs, I., Pluyter, J. R., Mavroeidis, D., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134209 · Cited by 26

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

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One-Sample Diffusion Modeling in Projection Domain for Low-Dose CT Imaging

Huang, B., Lu, S., Zhang, L., Lin, B., Wu, W., & Liu, Q. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3392248 · Cited by 27

Efficient Constrained Signal Reconstruction by Randomized Epigraphical Projection

Ono, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682191 · Cited by 26

TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

Noyan, M. A., Durdu, M., & Eskiocak, A. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-75546-z · Cited by 27

Deep learning and medical diagnosis

Liu, Y., Liu, G., & Zhang, Q. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32501-2 · Cited by 26

Data set terminology of deep learning in medicine: a historical review and recommendation

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Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?

Jiang, S., Jia, Q., Peng, Z., Zhou, Q., An, Z., Chen, J., & Yi, Q. (2025) · Schizophrenia · doi:10.1038/s41537-025-00583-4 · Cited by 26

Unsupervised 3D Brain Anomaly Detection

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FDA-approved deep learning software application versus radiologists with different levels of expertise: detection of intracranial hemorrhage in a retrospective single-center study

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Echocardiography-based AI for detection and quantification of atrial septal defect

Lin, X., Yang, F., Chen, Y., Chen, X., Wang, W., Li, W., Wang, Q., Zhang, L., Li, X., Deng, Y., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.985657 · Cited by 25

Automatic Detection of Peripheral Retinal Lesions From Ultrawide-Field Fundus Images Using Deep Learning

Tang, Y.-W., Ji, J., Lin, J., Wang, J., Wang, Y., Liu, Z., Hu, Z., Yang, J.-F., Ng, T. K., Zhang, M., et al. (2023) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000599 · Cited by 26

Enhancing Cervical Pre-Cancerous Classification Using Advanced Vision Transformer

Darwish, M., Altobel, M. Z., & Abiyev, R. H. (2023) · Diagnostics · doi:10.3390/diagnostics13182884 · Cited by 25

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction

Li, Z., Chang, D., Zhang, Z.-X., Luo, F., Liu, Q., Zhang, J., Yang, G., & Wu, W. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3420411 · Cited by 25

Simultaneous Classification and Segmentation of Intracranial Hemorrhage Using a Fully Convolutional Neural Network

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Deep Volumetric Ambient Occlusion

Engel, D., & Ropinski, T. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030344 · Cited by 25

Cranial Implant Design via Virtual Craniectomy with Shape Priors

Matzkin, F., Newcombe, V., Glocker, B., & Ferrante, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64327-0_5 · Cited by 25

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images

Han, C. H., Kim, M., & Kwak, J. T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249450 · Cited by 25

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Hibi, A., Jaberipour, M., Cusimano, M. D., Bilbily, A., Krishnan, R. G., Aviv, R. I., & Tyrrell, P. N. (2022) · Medicine · doi:10.1097/md.0000000000031848 · Cited by 25

Very deep super-resolution for efficient cone-beam computed tomographic image restoration

Hwang, J. J., Jung, Y., Cho, B., & Heo, M.-S. (2020) · Imaging Science in Dentistry · doi:10.5624/isd.2020.50.4.331 · Cited by 24

Intelligent prediction of RBC demand in trauma patients using decision tree methods

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Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

Brossard, C., Lemasson, B., Attyé, A., de Busschère, J.-A., Payen, J., Barbier, E., Grèze, J., & Bouzat, P. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.666875 · Cited by 29

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Rajaei, F., Cheng, S., Williamson, C. A., Wittrup, E. M., & Najarian, K. (2023) · Diagnostics · doi:10.3390/diagnostics13091640 · Cited by 24

Artificial intelligence applied in acute ischemic stroke: from child to elderly

Pacchiano, F., Tortora, M., Criscuolo, S., Jaber, K., Acierno, P., Simone, M. D., Tortora, F., Briganti, F., & Caranci, F. (2023) · La radiologia medica · doi:10.1007/s11547-023-01735-1 · Cited by 26

Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Pisov, M., Goncharov, M., Kurochkina, N., Morozov, S. P., Gombolevskiy, V. А., Chernina, V., Vladzymyrskyy, A. V., Zamyatina, K., Chesnokova, A., Pronin, I., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33850-3_4 · Cited by 24

Examination-Level Supervision for Deep Learning–based Intracranial Hemorrhage Detection on Head CT Scans

Teneggi, J., Yi, P. H., & Sulam, J. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.230159 · Cited by 23

Intracerebral Hemorrhage: Advances, Knowledge Gaps, and Future Directions

Liu, T., Jiang, W., Zhang, M., Xiong, S., Chen, L., Chen, X., & Jiang, R. (2025) · MedComm · doi:10.1002/mco2.70436 · Cited by 23

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

Huang, K. T., Silva, M. A., See, A. P., Wu, K. C., Gallerani, T., Zaidi, H. A., Lu, Y., Chi, J. H., Groff, M. W., & Arnaout, O. M. (2019) · Journal of Neurosurgery Spine · doi:10.3171/2019.6.spine19463 · Cited by 23

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging

Zhu, G., Chen, H., Jiang, B., Chen, F., Xie, Y., & Wintermark, M. (2022) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2022.02.004 · Cited by 24

Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage

Yuan, J. Y., Chen, Y., Jayaraman, K., Kumar, A., Zlepper, Z., Allen, M., Athiraman, U., Osbun, J. W., Zipfel, G. J., & Dhar, R. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.10.105 · Cited by 23

A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation

Nijiati, M., Tuersun, A., Zhang, Y., Yuan, Q., Gong, P., Abulizi, A., Tuoheti, A., Abulaiti, A., & Zou, X. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.977427 · Cited by 23

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

Koch, A. H., Jeelof, L. S., Muntinga, C. L. P., Gootzen, T. A., van de Kruis, N. M. A., Nederend, J., Boers, T. G. W., van der Sommen, F., & Piek, J. M. J. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01345-x · Cited by 25

Digital Frontiers in Healthcare: Integrating mHealth, AI, and Radiology for Future Medical Diagnostics

Najjar, R. (2024) · Biomedical engineering · doi:10.5772/intechopen.114142 · Cited by 25

Deep learning and medical diagnosis

Dewey, M., & Schlattmann, P. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32498-5 · Cited by 23

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans

Chen, Y., Chen, C.-C., Kuo, C., & Lin, C.-H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108587 · Cited by 22

DIY AI, deep learning network development for automated image classification in a point‐of‐care ultrasound quality assurance program

Blaivas, M., Arntfield, R. T., & White, M. R. (2020) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12018 · Cited by 24

Using machine learning to predict severe hypoglycaemia in hospital

Fralick, M., Dai, D., Pou-Prom, C., Verma, A. A., & Mamdani, M. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14472 · Cited by 23

Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture

Laverde‐Saad, A., Jfri, A. H., García, R., Fernández, I. S., Martínez, C., Cembrero, H., Roustan-Gullón, G., & Alfageme, F. (2021) · Skin Research and Technology · doi:10.1111/srt.13086 · Cited by 23

Performance of deep learning in the detection of intracranial aneurysm: A systematic review and meta-analysis

Gu, F., Wu, X., Wu, W., Wang, Z., Yang, X., Chen, Z., Wang, Z., & Chen, G. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110457 · Cited by 22

Evaluation of image quality, organ doses, effective dose, and cancer risk from pediatric brain CT scans

Kiani, M., & Chaparian, A. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110657 · Cited by 22

A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images

Kaya, B., & Önal, M. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22864 · Cited by 22

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Alimu, P., Chen, F., Han, Y., Dai, J., Xie, C., Wang, J., Mao, Y., Chen, Y., Lu, Y., Lv, C., et al. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-539 · Cited by 22

The Building Blocks of Thought

Laurence, S., & Margolis, E. (2024) · doi:10.1093/9780191925375.001.0001 · Cited by 26

Doubly Weak Supervision of Deep Learning Models for Head CT

Saab, K. K., Dunnmon, J., Goldman, R. E., Ratner, A., Sagreiya, H., Ré, C., & Rubin, D. L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_90 · Cited by 22

Enhancing Intracranial Hemorrhage Diagnosis through Deep Learning Models

Malik, P., Dureja, A., Dureja, A., Rathore, R. S., & Malhotra, N. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.157 · Cited by 21

DHI-GAN: Improving Dental-Based Human Identification Using Generative Adversarial Networks

Lin, Y., Fan, F., Zhang, J., Zhou, J., Liao, P., Chen, H., Deng, Z., & Zhang, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3159781 · Cited by 21

Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm

Yu, W., Kang, H., Sun, G., Liang, S., & Li, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3228003 · Cited by 21

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study

Cui, Y., Zhao, K., Meng, X., Mao, Y. J., Han, C., Shi, Z., Yang, X., Tong, T., Wu, L., & Liu, Z. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001161 · Cited by 21

The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview

Liu, F., Yao, Y., Zhu, B., Yu, Y., Ren, R., & Hu, Y. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1269742 · Cited by 21

Attention-based full slice brain CT image diagnosis with explanations

Fu, G., Li, J., Wang, R., Ma, Y., & Chen, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.044 · Cited by 21

Artificial Intelligence Approach in Melanoma

Curiel‐Lewandrowski, C. N., Novoa, R. A., Berry, E., Celebi, M. E., Codella, N., Giuste, F., Gutman, D. A., Halpern, A. C., Leachman, S. A., Liu, Y., et al. (2019) · Springer eBooks · doi:10.1007/978-1-4614-7322-0_43-1 · Cited by 20

Performance testing of a novel deep learning algorithm for the detection of intracranial hemorrhage and first trial under clinical conditions

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