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From toy to tool: the development of immersive virtual reality environments for psychotherapy of specific phobias.

Virtual Reality (VR) entered the mental health field some years ago. While the technology itself has been available for more than ten years now, there is still a certain amount of uncertainty among researchers and users as to whether VR will one day fulfill all it's promises. In this chapter we are giving an overview of the implementation of the technology in our mental health research facility in Basel, Switzerland. The development of two applications for use with claustrophobic and acrophobic patients perspectively serves just as an example within this context. Some may say, the chapter is t

Computer based nursing documentation means to achieve the goal.

New functions have been integrated in the Giessen Hospital Information System WING to support the classification of all intensive care patients into the Therapeutic Intervention Scoring System (TISS). The use of those functions has been pushed when health insurance bodies demanded evidence for the correct classification of ICU beds. This article presents an overview on this development from the start in just one intensive care unit to the complete coverage of six intensive care units and three intensive monitoring units with a total of 109 beds. For those units complete TISS data has been docu

Medical counseling through computer communication networks.

Medical counseling on the computer communication network will be used increasingly with wide-ranging concern of people about health and strengthening the role of medical care as social service. This study was designed to explore main problems/issues of network counseling and to suggest future prosperity of medical counseling. We analyzed questions and answers in the Association of Family Medicine in the KETEL since 1991, Medical counseling corner which is in the UNITEL since 1996, and Online counseling in VIRTUAL HOSPITAL since 1996, and classified the reason for encounter, systematic classifi

BASCD surveys and computer communication. British Association for the Study of Community Dentistry.

The British Association for the Study of Community Dentistry (BASCD) has coordinated National Health Service surveys of the dental health of school children since 1985, when Wales and parts of England were included. Scotland joined in 1987 and Northern Ireland in 1994. This series spans an interesting time in the growth of accessible technology in both statistics and medical research. The purpose of this paper is to chart the development of this data base from pen and paper via microcomputers to the Internet. The annual work now involves calibration of examiners, data collection with portable

'Shared care' and computer assistance in glaucoma management.

Primary open angle glaucoma afflicts 1-2% of people over 50 years of age. The diagnosis relies on a number of examinations, many of them performed by ophthalmic nurses, and the care of glaucoma patients has become one of their main tasks. A knowledge-based system for decision support in glaucoma management has been developed and validated. The aim of the current study is to evaluate the influence of computerised decision support on a 'shared care' organisation for the management of glaucoma patients.

Color perimetry with personal computer.

Color visual field analysis has proven highly sensitive for early visual impairments diagnosis in M. S., yet it has never attained widespread popularity usually because the procedure is difficult to standardize, the devices are costly, and the test is fatiguing. We propose a computerized procedure running on standard PC, cost effective, clonable, and easy handled. 264 colored patches subtending 1 degree angle of vision, with selected hues and low saturation levels are sequentially and randomly displayed on gray equiluminous background of the PC screen subtending 24 degrees x 40 degrees angle o

The use of an expert system of composite risk factors in breast cancer screening.

Mass Screening seems to be the only promising way to discover breast cancer patients at an early and more curable stage and a positive method improving the cost-effectiveness and compliance of mass screening is the use of prognostic factors, to identify the high-risk group, who alone then would be screened. In a 200 women sample, who had undergone screening for breast cancer with clinical examination and bilateral mammography, we calculated the Composite Risk Factors of six Characteristics (C6RF), which are family history for breast cancer, pregnancy history, menstrual history, history of cyst

Visualization of clinical data with neural networks, case study: polycystic ovary syndrome.

In medicine, the use of neural networks has concentrated mainly on classification problems. Clinicians are often interested in knowing what a patient's status is compared with other similar cases. Compared with biostatistics neural networks have one major drawback: the reliability of the classification is difficult to express. Therefore, clear visualization of the measurements can be more helpful than the calculated probability of a disease. The self-organizing map is the most widely used neural network for data visualization. Although, visualization can be attached to almost any feed-forward

Computer assisted therapy for multiple sclerosis and spinal cord injury patients application of virtual reality.

For patients with multiple sclerosis and spinal cord injury, virtual reality systems provide new methods of assistance with dysmetria, tremor, spasticity, and weakness. Robust mechanisms exist within the central nervous system to produce neuroplastic adaptive responses operative in retraining motor activities. Haptic systems cued by the patient's visual environment can produce force corridors to guide a patient's wrist and hand in the performance of specific tasks. Such haptic application can substantially reduce motor instability and improve performance. Preliminary clinical approaches, using

CAD modeling of the birth process. Part II.

We are engaged in studying the process of human birth by developing a computer-based dynamic model that can be adapted to specific birth scenarios. The individual characteristics of a given pregnant woman and fetus, embedded in their clinical measurements and CT and MRI images, are captured in the model. One can thereby predict adverse events that might happen during labor and delivery. Based on our findings from a preliminary design and execution of this model, we believe it holds great promise as an accurate, cost-effective diagnostic and teaching tool that will help predict conditions durin

Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.

IntroductionArtificial intelligence (AI) is a branch of technology enabling machines to emulate complex human skills; it can also entail problem-solving using bioinspired methods. It is used for automating systematic literature reviews (SLR), that is, defining a clinical question, locating relevant literature, preliminary screening, study evaluation, data extraction and analysis. Title and abstract screening is one of the most time-consuming and error-prone phases involved in developing a systematic review. While AI promises to expedite this process, adopting it faces challenges due to concern

Baseline Characteristics of Participants in the Exercise for Cancer to Enhance Living Well (EXCEL) Study: A Canada-Wide Rural-Urban Analysis.

IntroductionExercise interventions improve quality of life and survival for individuals living with and beyond cancer (ILWBC), yet equitable access remains limited. Evidence on characteristics of who enrolls in exercise oncology programs is scarce, leaving gaps for equity-focused recruitment and scale-up, particularly in rural and underserved settings. The EXercise for Cancer to Enhance Living Well (EXCEL) study offers a unique opportunity to examine these issues across a Canada-wide cohort.MethodsEXCEL is an 8-12-week tailored exercise intervention delivered primarily to ILWBC in rural/remote

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… G., “Research Electronic Data Capture (REDCap)‐A Metadata‐Driven Methodology and Workflow Process for Providing Translational Research Informatics Support,” Journal of Biomedical Informatics 42, no. 2 (2009): 377–381, 10.1016/j.jbi.2008.08.010. 23. Harris P. A., Taylor R., Minor B. … G., Hripcsak G., Swaminathan S., and Weng C., “Defining and Measuring Completeness of Electronic Health Records for Secondary Use,” Journal of Biomedical Informatics 46, no. 5 (2013): 830–836, 10.1016/j.jbi.2013.06.010. …

Diagnostic Performance and Concordance of a BI-RADS-Output AI System in Mammography: A Retrospective Study of Radiologists-AI Collaboration.

The identification of breast cancer using mammography and ultrasonography is influenced by inter-observer variability. Artificial intelligence (AI) models for breast classification have been investigated, but limited reports exist on Breast Imaging Reporting and Data System (BI-RADS)-output AI systems. This study attempted to assess a BI-RADS-output AI system by comparing breast cancer detection rates among radiologist-only, AI-only, and combined interpretations, while also focusing on the AI's ability to identify cancers missed by radiologists and analyzing diagnostic concordance concerning i

Annotation-Efficient Individual Muscle Segmentation from Dixon MRI: A Multi-Dataset Benchmark of Pretraining and Fat-Fraction Guidance.

Measuring individual muscles and their fatty infiltration on Dixon MRI can support sarcopenia assessment, but manual annotation is time-consuming and annotated datasets are often small. This study investigated accurate individual-muscle segmentation with limited labelled data. Three datasets were used: an abdominal CT dataset of 300 subjects with adipose-tissue labels, a lower-limb Dixon MRI dataset of 25 subjects with 13 thigh and 9 calf muscles, and an independent cohort of 66 subjects for external testing. Network architecture, encoder pretraining, a Dixon-derived fat-fraction input, and a

Hair Segmentation and Skin Characterization on Macroscopic Dermatological Images for Laser Treatment Parameterization.

Current light-based devices, such as laser hair removal, intense pulsed light (IPL), vascular laser therapy, and dermatologic phototherapy, are widely used in clinical dermatology but rely heavily on subjective manual parameterization or uncalibrated point-based colorimeters. To overcome these limitations, this work presents a unified system for hair segmentation and skin characterization in macroscopic dermatological images. Unlike most existing literature that relies on dermoscopic datasets captured under highly controlled conditions, our approach is specifically designed for macroscopic ima

Computer-Aided Diagnosis of Clinically Significant Prostate Cancer in Transrectal Ultrasound.

Diagnosing clinically significant prostate cancer (csPCa) using transrectal ultrasound (TRUS) remains a challenging task due to limited image contrast and the subtle appearance of malignant lesions. In this study, we propose a novel hybrid framework that integrates multi-modality and multi-task learning for csPCa classification from TRUS data. The framework leverages complementary information from B-mode and strain elastography (SE) imaging via a dual-stream encoder, enhanced by cross-modal attention gate (CMAG) modules at each level. These CMAG modules first adaptively integrate cross-modal f

Diagnosis of Suspected Pituitary Microadenomas: The Complementary Role of Habitat-based Non-enhanced MRI Radiomics Features.

To develop and validate a combined model for detecting pituitary microadenomas in suspected cases using non-contrast T1-weighted sagittal (T1SAG) magnetic resonance imaging (MRI), and to assess its performance as a non-contrast imaging-based tool for clinical decision support.This retrospective two-center study enrolled 519 patients with suspected pituitary microadenomas, divided into a training set (n = 321), an internal validation set (n = 80), and an external test set (n = 118). Radiomics features were extracted from both the entire pituitary gland and its habitat subregions, generated via

TriKey AutoRefine: A Stability-Aware Framework Built on Pan-organ Segmentation Radiomics for Chronic Kidney Disease Staging.

Accurate and generalizable imaging‑based staging of chronic kidney disease remains challenging due to segmentation variability, feature instability, and heterogeneous clinical presentations. To address these gaps, a clinically aligned pan‑organ radiomics workflow for multi‑class chronic kidney disease staging was proposed. A total of 587 subjects from 3 centers were included, with chest and abdominal computed tomography scans paired with clinical and body-composition measurements. Segmentation‑induced variability was quantified within the pan‑organ segmentation system, revealing systematic ana

Development and External Validation of a Machine Learning-Derived Risk Stratification Score for Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Metastatic castration-resistant prostate cancer (mCRPC) shows marked clinical heterogeneity and poor long-term survival, highlighting the need for simple and reliable risk stratification tools beyond the limited usability of existing complex prognostic models. In this study, we developed and validated PROGRESS (Prostate Cancer Global Risk Evaluation and Stratification Score), a simplified prognostic score derived through machine learning‑guided feature selection distilled into a simple laboratory-based tool. METHODS: Baseline data from 2,035 patients with mCRPC (training set) were em

Field-Strength Dependence of Automated Subcortical Volumetry: A Paired 3 T-7 T Benchmarking Study of BrainSuite, FastSurfer and NextBrain.

Automated segmentation pipelines are widely used for quantitative assessment of subcortical brain structures, yet it remains unclear whether measurements obtained at different magnetic field strengths are numerically interchangeable. This study evaluated the cross-field agreement and inter-software consistency of automated subcortical volumetry using paired 3T and 7T structural MRI data from 30 healthy adults. T1-weighted images were independently processed using BrainSuite, FastSurfer and NextBrain. Volumes of the bilateral putamen, globus pallidus, nucleus accumbens, thalamus and caudate nuc