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Background: Three-dimensional (3D) image technology in breast measurement requires exploration. We aimed to evaluate a new automatic breast measurement system based on artificial intelligence (AI).
Methods: This prospective controlled study included all-women patients who underwent breast reconstruction from January to May 2022. Patients underwent 3D scanning before breast reconstruction. Two doctors performed the measurements twice through AI and manual measurements on the 3D images, respectively. The measurement results of bilateral breast width, convexity, height, volume, and measurement time were recorded. Consistency analyses were performed.
Results: Fifty-eight patients (116 breasts) were recruited. For the left breasts, AI and manual measurements showed excellent consistency (intra-class correlation coefficients (ICC) = 0.81) in width measurements, moderate consistency (ICC = 0.59) in height measurements, excellent consistency (ICC = 0.87) in convexity measurements, and good consistency (ICC = 0.74) in volume measurements. For the right breasts, the width consistency was excellent (ICC = 0.93), height consistency was good (ICC = 0.65), convexity consistency was excellent (ICC = 0.94), and volume consistency was excellent (ICC = 0.85). The Bland-Altman curves also showed that the measurement results were comparable and few outliers were detected. AI average measurement time (compared to manual measurements) was significantly shorter (40.65 ± 1.51 s vs. 610.47 ± 18.74 s; p < 0.001).
Conclusion: The AI-based 3D breast measurement system showed high accuracy, better reproducibility, and significantly shortened the measurement time, which could help guide surgical management.
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http://dx.doi.org/10.1016/j.jpra.2025.01.023 | DOI Listing |
Mult Scler
September 2025
Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, VA Medical Center, TN Valley Healthcare System, Nashville, TN, USA.
Background: There is limited knowledge on the post-glymphatic structures such as the parasagittal dural (PSD) space and the arachnoid granulations (AGs) in multiple sclerosis (MS).
Objectives: To evaluate differences in volume and macromolecular content of PSD and AG between people with newly diagnosed MS (pwMS), clinically isolated syndrome (pwCIS), or radiologically isolated syndrome (pwRIS) and healthy controls (HCs) and their associations with clinical and radiological disease measures.
Methods: A total of 69 pwMS, pwCIS, pwRIS, and HCs underwent a 3.
Int J Gen Med
September 2025
Department of Neurology, Aerospace Center Hospital, Beijing, 100049, People's Republic of China.
Acute vestibular syndrome (AVS) is characterized by the sudden onset of dizziness or vertigo, accompanied by nausea, vomiting, gait instability, and nystagmus, lasting for more than 24 hours and often persisting for several days to weeks. Central AVS primarily involves central vestibular structures, such as the brainstem and cerebellum, and is most commonly caused by ischemic stroke in the posterior circulation. When acute posterior circulation infarction presents solely with isolated dizziness or vertigo, without other symptoms of central nervous system damage, it is often misdiagnosed as a peripheral vestibular disorder, this can lead to serious consequences.
View Article and Find Full Text PDFNeurotrauma Rep
August 2025
Department of Radiology, Weill Cornell Medicine; New York, New York, USA.
Traumatic brain injury (TBI) impairs attention and executive function, often through disrupted coordination between cognitive and autonomic systems. While electroencephalography (EEG) and pupillometry are widely used to assess neural and autonomic responses independently, little is known about how these systems interact in TBI. Understanding their coordination is essential to identify compensatory mechanisms that may support attention under conditions of neural inefficiency.
View Article and Find Full Text PDFFront Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.