e.g. mhealth
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Taking random cropping as an example, the size of the original image was reset to 512×512×3, and after random cropping, the size was 256×256×3. The size of the augmented image was fixed again to 224×224×3 before being input into the networks so that the network model could recognize them.
Image augmentation: (A) original image, (B) horizontal flip, (C) vertical flip, and (D) random clipping.
The PI images are RGB color patterns (Figure 3), and the number of pixels is [0, 256] [16].
JMIR Med Inform 2025;13:e62774
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However, manual and semi-automatic techniques are labor-intensive and time-consuming, making the image processing task for large studies difficult, expensive, and, most importantly, impractical to apply in a clinical setting. Therefore, in the present study, we aimed to use MRI scans of the head opportunistically to develop an automated deep learning method to evaluate sarcopenia.
JMIR Aging 2025;8:e63686
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Rather, errors primarily stem from the methods radiologists use to visually inspect the image, referred to as perceptual errors [4]. In other words, perceptual errors in radiology are mistakes that occur during the visual inspection and interpretation of medical images. They are distinct from cognitive errors, which involve incorrect reasoning or decision-making based on observed information.
JMIR Form Res 2025;9:e53928
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In patients with e PESI
Image of the best practice alert presented to clinicians, depicting the Electronic Pulmonary Embolism Severity Index (e PESI) score. CT: computed tomography; DOAC: direct oral anticoagulant; HR: heart rate; Hx: history; O2 Sat: oxygen saturation, as measured by pulse oximetry; PE: pulmonary embolism; RR: respiratory rate; SBP: systolic blood pressure; Suppl O2: supplemental oxygen; Temp: temperature.
JMIR Med Inform 2025;13:e58800
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client/server framework for the continuous collaborative improvement of deep-learning-based medical image Reference 34: MRtrix3: a fast, flexible and open software framework for medical image processing and Reference 35: Complex diffusion-weighted image estimation via matrix recovery under general noise models Reference 38: Advances in functional and structural MR image analysis and implementation as FSLimage
JMIR Rehabil Assist Technol 2025;12:e64825
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These images were generated using the Leonardo.ai platform [26], which harnesses the capabilities of the Stable Diffusion XL image–generating technology (Figure 3 and Multimedia Appendix 4).
In an effort to maintain transparency and distinguish between real and AI-generated content, all images depicting real people were marked with an “AI-generated image” icon. This icon, chosen for its symbolic significance, is the spinning top from the movie “Inception.”
JMIR Med Educ 2025;11:e63865
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The introduction of the image recognition feature further expands the horizon, opening up a new realm of applications in medical clinical practice and research [4]. Previous studies on LLMs have demonstrated their ability to pass medical licensing examinations [5-7]. However, these studies were often limited by the models’ restricted image analysis capabilities, leaving some questions unanswered [7].
JMIR Form Res 2024;8:e57592
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Uniformity was maintained while cropping the coronal image fields in all the CBCT scans so that the anatomical landmarks were consistent. Each image was then cropped into a 200 × 400–pixel square, extending from the crista galli superiorly to the hard palate inferiorly, and 5 mm laterally from the lateral nasal wall on both sides (Figure 1 A and 1 B). The files were saved in JPEG format.
Two maxillofacial radiologists classified the nasal septum images into normal or deviated.
JMIR Form Res 2024;8:e57335
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