Physical and Engineering Sciences in Medicine最新文献

{"title":"A method for the assessment of rheumatoid arthritis using neural network supported static and dynamic thermal analysis.","authors":"H Feza Carlak, Yakup Irim, Sevcan Uğur, Cahit Kaçar, Ayşe Ayça Yücel","doi":"10.1007/s13246-026-01734-2","DOIUrl":"https://doi.org/10.1007/s13246-026-01734-2","url":null,"abstract":"<p><p>Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by pain, swelling, stiffness, and loss of joint function, making early diagnosis challenging. The study aims to assess the differences between RA patients (n = 70) and healthy individuals (n = 30) while classifying Ritchie Articular Index (RAI) values (0-3) based on inflammation levels using artificial intelligence algorithms. Metacarpophalangeal (MCP), and proximal-interphalangeal (PIP) joints were analyzed for the degree of inflammation. Static thermal data was collected from individuals at rest in a controlled environment. Then, alcohol was applied to the participants' hand regions, followed by a 180-second thermal video recording of the same region. In the pre-processing step, background noise cleaning and alignment were performed. Background was eliminated using Snake algorithm. Thermal video recordings were aligned using Scale Invariant Feature Transform (SIFT) algorithm. The Skeletonization algorithm was employed to detect fingers and joint regions in the images. For static thermal analysis, initial temperature ([Formula: see text]) values were extracted from the resting thermogram data. In dynamic thermal analysis, the temperature parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were calculated. A statistical analysis of the four temperature parameters across different RAI values revealed that [Formula: see text] (p = 0.025) and [Formula: see text] (p = 0.042) exhibited statistically significant differences among the four RAI levels. Machine learning models were trained using the resting temperature values of patient and healthy groups, and the SVM achieved the highest success rate of 93%. It is believed that the proposed system may help diagnose RA in clinical settings and contribute to determining the severity of inflammation.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575879","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}

{"title":"Proposing diagnostic reference levels for paediatric computed tomography in Jordan: a national multicentre analysis.","authors":"Abdel-Baset Bani Yaseen, Jamie Trapp, Davide Fontanarosa","doi":"10.1007/s13246-026-01731-5","DOIUrl":"https://doi.org/10.1007/s13246-026-01731-5","url":null,"abstract":"<p><p>Children are more radiosensitive than adults, making dose optimisation in paediatric computed tomography (CT) essential. Although Diagnostic Reference Levels (DRLs) are internationally recommended as optimisation tools, national DRLs for paediatric CT in Jordan remain limited and outdated. This study aimed to establish national DRLs for six paediatric CT protocols, evaluate inter-hospital and age-related dose variations, and compare results with international benchmarks. A retrospective multicentre study was conducted across six Jordanian hospitals between December 2023 and September 2025, including 3794 paediatric patients stratified into four age groups (< 1, 1-4, 5-10, and 11-18 years). Volumetric CT dose index (CTDI_vol) and dose-length product (DLP) data were collected for six protocols: brain, chest, abdomen-pelvis, chest-abdomen-pelvis, sinuses, and contrast-enhanced neck soft tissue. DRLs were defined as the 75th percentile of institutional median CTDI_vol and DLP values. Inter-hospital and age group variations were analysed, and univariable regression analyses assessed acquisition parameters associated with dose variation. Dose increased with patient age for trunk protocols, whereas brain, sinuses, and neck CT showed comparatively stable patterns. Substantial inter-hospital variability was observed across protocols, with institutional median CTDIvol differing markedly between centres, particularly for trunk examinations in younger age groups. In univariable regression analyses, all four acquisition parameters (kVp, mAs, pitch, and slice thickness) were significantly associated with CTDI_vol and DLP (p ≤ 0.001), with kVp demonstrating the strongest association (R² = 0.603 for CTDI_vol; R² = 0.630 for DLP). Compared with published international DRLs, Jordanian brain CT dose metrics were higher in multiple age groups; chest comparisons should be interpreted cautiously where international benchmarks are weight-banded. This study established national paediatric CT DRLs for Jordan, highlighting the need for standardised protocols and periodic DRL review to enhance radiation protection.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575899","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}

{"title":"CNN1D-LSTM with SMOTE for breast cancer classification: performance and statistical insights.","authors":"Kamini G Panchbhai, Lalchand B Patle, Madhusudan G Lanjewar","doi":"10.1007/s13246-026-01724-4","DOIUrl":"https://doi.org/10.1007/s13246-026-01724-4","url":null,"abstract":"<p><p>Breast cancer is a significant global health issue, demanding early identification to provide appropriate therapy and satisfactory survival results. This work used two independent imbalanced datasets (EIS-BT and WBCD). CNN1D with Long Short-Term Memory (LSTM) was integrated to acquire features from these datasets to identify breast cancer. Three scenarios for breast cancer detection were investigated based on CNN1D-LSTM derived characteristics from Dataset-1, Dataset-2, and their combination. The Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the collected features in all three scenarios. The suggested CNN1D-LSTM-SMOTE approach, in conjunction with Support Vector Classification (SVC), yields impressive results with a Matthews Correlation Coefficient (MCC) of 97.2 % on Dataset-1 and 100.0% on Dataset-2. Random Forest Classifiers (RFC) perform better, achieving an MCC of 98.4% on the combined features. The K-fold approach was used, yielding average MCCs of 91.7%, 74.1%, and 96.9% on Dataset-1, Dataset-2, and the combined features, respectively. Statistical analysis revealed a p-value of 0.01, signifying the significance of the findings, and a standard error of 0.006 for the combined features. Bootstrapping was employed to calculate confidence intervals, resulting in Lower Confidence Intervals (LCI) of 95.6% and Higher Confidence Intervals (HCI) of 97.9% for the combined features. These findings highlight the model's potential clinical application in supporting oncologists with early, real-time, reliable, and automated breast cancer diagnosis, leading to improved diagnostic procedures and patient outcomes.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515945","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}

{"title":"Education note: cultivating assessment and feedback for learning in our training hospital.","authors":"Rebecca Day, Louise Beckingsale","doi":"10.1007/s13246-026-01721-7","DOIUrl":"https://doi.org/10.1007/s13246-026-01721-7","url":null,"abstract":"<p><p>High-value feedback is essential for the successful development of medical physics trainees (registrars) as they complete their clinical training. In this education note, we present an overview of a small education intervention we implemented in our hospital to encourage feedback conversations, one of the principles of high-value feedback. Our intervention was based on the concepts of 'assessment for learning' and 'programmatic assessment'. This intervention encourages registrars to take ownership of the feedback process associated with each learning outcome assessment. Registrars are encouraged to complete and document a self-assessment of their performance, before seeking feedback from their trainer. We modified an assessment template to support this process. This new approach appears to have resulted in higher quality feedback conversations between registrars and trainers. Additionally, trainers have benefited from the registrars completing the majority of the assessment template, thus reducing their workload. Whilst this approach has increased the workload for registrars, we believe this has benefits for their learning. We hope other centres can learn from our experience and may consider how they can adopt a similar process for their setting.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505236","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}

{"title":"A study on using custom 3D-printed tongue bites for radiotherapy patients with oral tongue carcinoma.","authors":"Muhmmad Furqan Zulqurnain, Haris Arif, Misbah Batool, Nouman Amjad, Robin Hill","doi":"10.1007/s13246-026-01725-3","DOIUrl":"https://doi.org/10.1007/s13246-026-01725-3","url":null,"abstract":"<p><p>Oral cavity cancers are a debilitating form of head and neck cancer with high rates of mortality. Radiotherapy is one of the main forms of treatment but relies on minimizing doses that are delivered to organs at risk and considering any motion in the mouth. One solution is to use a block within the mouth which acts to reduce motion and decreases tissue heterogeneity. In this work, we developed a process for designing a customized tissue-equivalent 3D-printed tongue bite and evaluated its impact on the radiation treatment. Six patients with stage III or IV oral cancer were involved. Computed tomography (CT) images for each patient were acquired with currently used Styrofoam tongue bites within the mouth. The designs of 3D tongue bites were prepared using those CT images and then printed on an SLA printer using F80 resin which is a tissue-equivalent and biocompatible material. Secondary CT images were then acquired for each patient with the 3D tongue bites to have a dosimetric comparison. Volumetric-modulated arc therapy (VMAT) planning was carried out for individual patients on both CT images. Plan parameters, fractionation scheme and optimization priorities were all kept the same. The radiotherapy plans utilizing 3D tongue bites showed better PTV coverage and reduced D_max (p = 0.028). Doses to organs at risk (OARs) including brainstem, parotid glands and hard palate were also reduced (p < 0.028) except for the spinal cord (p > 0.05). The dose conformity and homogeneity were also improved (p = 0.028 and p = 0.044 respectively). All patients reported that the 3D tongue bites were soft, conformal to the oral cavity, comfortable and did not cause any gag reflex. We conclude that the 3D tongue bite is a useful utility that improves the treatment of patients with oral cancer.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147487825","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}

{"title":"Intelligent system for infants' pain detection: pain intensity estimation using deep learning approach.","authors":"Mashhour Amer, Manal Kassab, Waed Alshurman","doi":"10.1007/s13246-026-01720-8","DOIUrl":"https://doi.org/10.1007/s13246-026-01720-8","url":null,"abstract":"<p><p>Pain detection is an important agent for good pain management, especially for patients who are unable to express pain verbally as infants. Recently, professionals have depended on traditional assessment tools to detect pain but these tools have many limitations that may lead to poor pain management. For that, many researchers intended to find approaches to detect pain without these limitations, and the Artificial Intelligence (AI) field is the best for that. In this study, we proposed a deep learning model to estimate different levels of the pain intensity of full-term infants in the range (0-9) based on facial expressions that had been recorded during daily medical procedures in the NICU. We built a regression CNN model with a transfer learning technique and used a pre-trained VGG16 model with fine-tuning to improve the performance of classification and avoid overfitting. The model yielded a good performance with 0.494 MAE and 0.435 MSE. This study can contribute to accurately detecting the pain for infants which assists in achieving effective pain management. And make the system portable and user-friendly by embedding the model into a web application.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147476044","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}

{"title":"Investigation of the effect of planning techniques on thyroid and lens absorbe doses in radiotherapy of left breast cancer by in vivo dosimetry: a prospective study.","authors":"Osman Vefa Gul, Hamit Basaran, Mursel Duzova, Cagdas Yavas","doi":"10.1007/s13246-026-01722-6","DOIUrl":"https://doi.org/10.1007/s13246-026-01722-6","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147444741","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}

{"title":"Quantitative evaluation of radiotherapy accuracy in head and neck cancer: correcting cbct image distortions for improved tumour targeting and dose assessment.","authors":"Reda Čerapaitė-Trušinskienė, Diana Meilutytė-Lukauskienė, Greta Karpavičienė, Monika Jonušaitė, Robertas Petrolis","doi":"10.1007/s13246-026-01726-2","DOIUrl":"https://doi.org/10.1007/s13246-026-01726-2","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436670","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}

{"title":"Dependence of motion artifacts on the starting angle of projection data collection in a nonhelical volume scan of a 320-row CT.","authors":"Yuho Kikuchi, Kazuhiro Sato, Toshimitsu Sato, Makoto Ohba, Koji Suzuki","doi":"10.1007/s13246-026-01719-1","DOIUrl":"https://doi.org/10.1007/s13246-026-01719-1","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147391444","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}

{"title":"Evaluating the impact of partial volume correction on FDG PET radiomics stability in lymphoma lesions.","authors":"Setareh Hasanabadi, Mohammad Saber Azimi, Mehrdad Bakhshayesh Karam, Hossein Arabi","doi":"10.1007/s13246-026-01717-3","DOIUrl":"https://doi.org/10.1007/s13246-026-01717-3","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147391421","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}