IEEE Pulse最新文献_第6页

From PET to Prescription: Building a High-Reliability Radiopharmaceutical Service Line. 从PET到处方：建立高可靠性的放射性制药服务专线。

IF 1.2 4区医学

IEEE Pulse Pub Date : 2025-11-01 DOI: 10.1109/MPULS.2025.3640848

Tejas Padliya

{"title":"From PET to Prescription: Building a High-Reliability Radiopharmaceutical Service Line.","authors":"Tejas Padliya","doi":"10.1109/MPULS.2025.3640848","DOIUrl":"10.1109/MPULS.2025.3640848","url":null,"abstract":"Radiopharmaceuticals turn nuclear physics into a clinical logistics problem: the moment an isotope is created, decay drives the timetable for synthesis, testing, transport, and infusion. This article provides a practical blueprint for health systems to build and scale a high-reliability service line that delivers on that timetable. Using prostate-specific membrane antigen (PSMA) theranostics as an exemplar, we translate half-life constraints for $^{18}text{F}$ , 68Ga, 64Cu, 89Zr, and 177Lu into operational design choices spanning cyclotron/generator sourcing, automated synthesis, aseptic processing, and rapid but robust quality control under the FDA. We explain why TLC alone can miss clinically relevant radiolysis products and show how paired HPLC methods, environmental monitoring, and prestaged release packets prevent day-of-care failures. On the clinical side, we align procedure guidelines, emerging posttherapy imaging standards, and pragmatic dosimetry ladders to help teams implement closed-loop, patient-specific planning without overwhelming resources. We also map radiation protection practices for safe outpatient delivery and examine supply-chain realities for 177Lu and 225Ac, emphasizing redundancy and scheduling discipline. The result is an engineering-forward, evidence-anchored roadmap that connects PET images to reliable therapy, thereby improving access, safety, and measurable outcomes.","PeriodicalId":49065,"journal":{"name":"IEEE Pulse","volume":"16 6","pages":"30-34"},"PeriodicalIF":1.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147370498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Unveils Health Predictions From EEG and MRI Data. 深度学习揭示从脑电图和核磁共振数据的健康预测。

IF 0.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618430

Raheel Zafar, Hakim Abdulrab

{"title":"Deep Learning Unveils Health Predictions From EEG and MRI Data.","authors":"Raheel Zafar, Hakim Abdulrab","doi":"10.1109/MPULS.2025.3618430","DOIUrl":"10.1109/MPULS.2025.3618430","url":null,"abstract":"The field of neuroscience and neuroimaging has been revolutionized with the use of artificial intelligence (AI), as it helps in enhancing the detection of brain activities and accurately diagnosing neurological disorders using various modalities. There are different modalities that help in measuring brain activities, but the most common and widely used are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The advanced AI approaches, like deep learning (DL) models, give a new opportunity to various fields, including brain research. This research investigates various AI-driven techniques used for the detection and exploration of the human brain using fMRI and EEG. The AI methods include different machine learning (ML) and DL techniques used to interpret neural activities. Basically, the AI-based models, which also include ML and DL, identify the patterns and detect the abnormalities with higher accuracy, which is helpful in many applications, including brain decoding, monitoring cognitive states, brain-computer interface (BCI), and diagnosis of various diseases. This research provides a comprehensive overview of AI applications in neuroimaging, highlights key applications in cognitive neuroscience and medical imaging, along with a discussion of challenges and future directions. The AI impact of the transformation of neuroimaging research is comprehensively discussed with examples to enhance comprehension.","PeriodicalId":49065,"journal":{"name":"IEEE Pulse","volume":"16 5","pages":"27-34"},"PeriodicalIF":0.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rebuilding Lives With Wearables: The Next Frontier in Rehabilitation. 用可穿戴设备重建生活：康复的下一个前沿。

IF 1.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618424

Jim Banks

引用次数: 0

Toward Personalized Healing: AI-Supported Wearables in Mental Health Practice. 走向个性化治疗：心理健康实践中人工智能支持的可穿戴设备。

IF 0.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618429

Mary Bates

引用次数: 0

One Patient, Two Worlds: Digital Twins for Everyday Prevention and Care. 一个病人，两个世界：日常预防和护理的数字双胞胎。

IF 0.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618425

Tejas Padliya

引用次数: 0

Listening for COVID: Noninvasive Detection From Cough and Breath Sounds. 聆听COVID：从咳嗽和呼吸声音中进行无创检测。

IF 1.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618438

Devanshi Mallick, Aarush Aggarwal, Eddie Yin-Kwee Ng, Vinay Arora

{"title":"Listening for COVID: Noninvasive Detection From Cough and Breath Sounds.","authors":"Devanshi Mallick, Aarush Aggarwal, Eddie Yin-Kwee Ng, Vinay Arora","doi":"10.1109/MPULS.2025.3618438","DOIUrl":"10.1109/MPULS.2025.3618438","url":null,"abstract":"Traditional procedures for diagnosing COVID-19 have mostly depended on invasive or resource-intensive technologies such as X-ray imaging, computed tomography (CT) scans, magnetic resonance imaging (MRI), and reverse transcription polymerase chain reaction (RT-PCR). Although these methods have therapeutic value, they are sometimes impractical for widespread screening, especially in settings with low resources or remote areas where access to highly skilled specialists and advanced technology is scarce. This work investigates noninvasive, AI-driven pipelines for COVID-19 detection that use cough and breath sounds as the primary inputs to address these issues. The proposed approach starts with sound acquisition and moves on to comprehensive feature extraction, focusing on image-based audio representations. The ability of various techniques, including scalograms, spectrograms, mel-spectrograms, chromagrams, wavelet spectrograms, cepstral analysis, gammatonegrams, power spectrograms, and short-time Fourier transform (STFT), to produce discriminative features that can either parallel or even enhance radiological modalities in AI-assisted systems is assessed.","PeriodicalId":49065,"journal":{"name":"IEEE Pulse","volume":"16 5","pages":"52-58"},"PeriodicalIF":1.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Melanoma Detection to Diabetes Monitoring: The Promise of Microneedle Patches. 从黑色素瘤检测到糖尿病监测：微针贴片的前景。

IF 1.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618457

Janet Rae-Dupree

引用次数: 0

Engineering Deep Dive of the VascuMAP Development. VascuMAP开发的工程深入研究。

IF 1.2 4区医学

IEEE Pulse Pub Date : 2025-09-01 DOI: 10.1109/MPULS.2025.3618448

James Stewart Campbell

引用次数: 0

AI in Health Care: Opportunities and Risks in Low- and Middle-Income Countries. 卫生保健中的人工智能：低收入和中等收入国家的机遇和风险。