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Towards computer assisted evaluation of plagiocephaly.

Plagiocephaly is a common asymmetry of the skull giving the impression that the subject, usually a neonate, has suffered a compressive force acting unilaterally. Normally innocuous, it is a postural deformity that can be suggestive of other significant abnormalities, e.g. joint dysplasia. Although intuitive measurements of plagiocephaly appear in the literature there is little evidence of analytical evaluation. This paper describes a measurement methodology based on black and white photography. A flat-bed scanner and a personal computer facilitated the transformation of the prepared photograph

The development of personal computer-based medical consultation system for diagnosis of congenital malformation syndromes using MUMPS.

This paper describes a medical consultation system for diagnosis of congenital malformation syndromes. The system consists of a personal computer, NEC PC-98XL (MS-DOS), using SP-MUMPS for the retrieval of knowledge data, and BASIC for the input/output of image data. This system displays pictorial knowledge and is designed so users can build up the knowledge base, as needed, by adding knowledge from medical literature and records of patients actually treated.

Predicting the potential for recovery within 24 hours of a head injury.

A linear discriminant model is applied to clinical and radiological signs from 492 consecutive head injuries transferred to a neurosurgical unit over a period of four years. Unstable discriminators may be eliminated and those remaining transformed to interval scales in order to release their full prognostic potential. Patient selection criteria included being in coma, judged in the accident and emergency (A & E) department to be susceptible to developing a complicating secondary event, having a depressed fracture and presenting evidence of leakage of cerebrospinal fluid. These criteria are con

Modelling endemic onchocerciasis in man in the presence of vector controls.

A stochastic model for a parasitic disease is proposed which describes the acquisition of infectious material from an external source and the subsequent deterioration of the host reacting to the internally produced parasite. The model considers the endemic situation, where the disease is uncontrolled and the structure is both hierarchical and irreversible. The resulting compartmental model can be modified to incorporate piecewise-constant migration rates to respond to possible geographical and sociological fluctuations, which could affect the epidemiological dynamics. The model is illustrated

An automated procedure for determining patient numbers from episode of care records.

Records of in-patient stays which may contain multiple entries for the same patient are linked. These provide a profile of all in-patient hospital contacts for the same patient and statistics from which prevalence measures can be estimated. The linkage procedure is designed to link Scottish Morbidity Records for patients of the Greater Glasgow Health Board. It is suited to in-patient records generated from a relatively small number of hospitals (25-30). The records are selected by diagnosis. A suite of computer programs automates the method, which was initially investigated manually. Two types

Integrating Academic and Community Cancer Care and Research through Multidisciplinary Oncology Pathways for Value-Based Care: A Review and the City of Hope Experience.

As the US transitions from volume- to value-based cancer care, many cancer centers and community groups have joined to share resources to deliver measurable, high-quality cancer care and clinical research with the associated high patient satisfaction, provider satisfaction, and practice health at optimal costs that are the hallmarks of value-based care. Multidisciplinary oncology care pathways are essential components of value-based care and their payment metrics. Oncology pathways are evidence-based, standardized but personalizable care plans to guide cancer care. Pathways have been developed

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… Not shown in Figure 1 are the enterprise-wide initiatives to engage and measure patient and staff satisfaction, institutional health as well as the administrative support needed in informatics, precision medicine, EHR enhancements, care models, and payer contracting to fully meet VBCC goals. … As they are developed and implemented with structured orders in the EHR from paper orders, the informatics, analytic, and finance teams are getting engaged to develop outcome reports. …

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program.

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and s

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… JCO Clinical Cancer Informatics . 2019;3(3):1–9. doi: 10.1200/cci.18.00001. 19. Davenport T., Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal . 2019;6(2):94–98. doi: 10.7861/futurehosp.6-2-94. 20. Romm E. L., Tsigelny I. F. … Journal of the American Medical Informatics Association . 2014;21(e2):e278–e286. doi: 10.1136/amiajnl-2013-002512. 47. Kastrin A., Ferk P., Leskošek B. Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. …

Knowledge-Guided Skills Agent for Microbial Colony Detection in Automated Laboratory Testing.

Automated microbial colony detection must remain reliable under heterogeneous imaging conditions while preserving deployed detector parameters. We propose ColonyAgent, a knowledge-guided preprocessing framework that combines quality assessment, retrieval of reusable skills and experiences, LLM-assisted workflow planning, and label-free feedback refinement. Experiments on AGAR and a self-built dataset use YOLOv8, RT-DETR, and Faster R-CNN with three independently trained checkpoints per architecture. Relative to direct inference, ColonyAgent improves mean image-macro mAP@0.5 by 0.1419-0.1706 on

Comparative analysis of generative pre-trained transformers for text- and image-based cephalometric prompts using a novel Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI).

INTRODUCTION: The aim of this study was to compare the response capabilities of generative pre-trained transformers (GPTs) for identical lateral cephalograms (LCs) based on text- and image-based prompts, using the newly developed Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI). METHODS: A total of 90 LCs from 30 cases each with skeletal Class I, II, and III malocclusions were included. The LCs were presented to GPT-4o, GPT-o3 pro, GPT-5, and GPT-5 pro in two different formats: text-based (numerical data including cephalometric analysis measurements) and image-base

ScanSeg: Policy-Guided Sequential Prompting for Dense Fine-Grained Ultrasound Segmentation.

Dense fine-grained segmentation on standardized ultrasound planes is clinically essential but remains challenging because multiple anatomical structures coexist under speckle noise, acoustic shadowing, weak boundaries, and partial visibility. Promptable foundation models can produce high-quality masks when queried, but they typically return one structure at a time and lack a mechanism to automatically decide what to segment next and where additional evidence is needed, making single-shot "auto" outputs brittle on complex planes. This paper introduces ScanSeg, a scan-inspired framework that for

Uncertainty-guided Multitask Mean Teacher Model for Hybrid ASPECTS Scoring of Acute Ischemic Stroke.

The Alberta Stroke Program Early CT Score (ASPECTS) is crucial for accurate diagnosis and treatment decision-making in acute ischemic stroke. Recent hybrid scoring methods that combine ASPECTS prediction with auxiliary lesion segmentation have demonstrated great potential by enhancing the model's discriminative power through multitask learning. However, their performance is still limited by the scarcity of labeled data and the insufficient exploitation of shared information across tasks, posing challenges for precise and robust stroke assessment. In this paper, we propose a novel semi-supervis

Interpretable machine learning model for early prediction of deep vein thrombosis in elderly ICU patients: multicenter development and external validation.

BACKGROUND: Deep vein thrombosis (DVT) is a frequent complication in elderly intensive care unit (ICU) patients, while reliable prediction tools for this population remain limited. This study aimed to develop and externally validate an interpretable machine learning model for early prediction of DVT in critically ill elderly patients. METHODS: In this multicenter retrospective study, ICU patients aged ≥ 75 years from two tertiary medical centers in China were enrolled. Patients with pre-existing DVT were excluded. A development cohort (Suzhou, n = 1,717) and an external validation cohort (Xi'

VARAL: Vision-Based Autonomous Recognition of Airway Landmarks for Endotracheal Intubation.

Autonomous recognition of anatomical landmarks in the airway plays a significant role in assisting clinicians to perform a video-guided endotracheal intubation (ETI) rapidly and accurately, which can increase its efficiency and success rate. However, existing recognition models still can not identify all anatomical landmarks of the airway, and their clinical assistance effects have not been quantitatively evaluated, which seriously restricts their clinical applications. This study proposes a vision-based autonomous recognition system for airway landmarks (VARAL system) based on YOLOv5 structur

Q-AortaECG: A Quantum-Inspired Multi-Agent 12-Lead ECG System for Early Triage of Acute Aortic Syndrome.

Acute aortic syndrome (AAS) is a time-critical vascular emergency in which delayed recognition may occur because the presenting symptoms and electrocardiographic phenotypes often overlap with acute coronary syndrome, pulmonary embolism, pericarditis, and other emergency diagnoses. Computed tomographic angiography (CTA) remains the confirmatory standard, but the 12-lead electrocardiogram (ECG) is usually obtained earlier, is universally available at triage, and may carry distributed signatures of malperfusion, ischemia mimicry, autonomic stress, conduction delay, and hemodynamic compromise. We

EEG-Based Characterization of Cortical Dynamics during Graded Resistance Exercise.

Understanding how brain activity varies during voluntary movement under different resistance conditions may provide insights into neural responses associated with physical demands and inform the development of neurorehabilitation strategies. In this study, we investigated changes in neural activity associated with increasing resistance levels during unilateral bicep curl movement. Ten healthy participants performed bicep curls under five resistance conditions while their cortical activity was recorded using electroencephalography (EEG). Movement-related cortical potentials (MRCPs), power spect

Reducing overconfident errors in clinical prediction models.

Machine Learning (ML) models are increasingly being used in clinical workflows. Evaluation of these models tends to focus on global performance metrics, which can obscure error patterns and lead to bias in clinical decision making. Here, we propose Proximal Error-Based Confidence Adjustment (PECA), a framework designed explicitly to improve the safety of ML predictions by reducing a model's confidence in regions of the feature space associated with historical prediction errors. Rather than optimizing global metrics alone, PECA targets confident misclassifications by attenuating prediction conf

Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.

BACKGROUND: Cardiovascular disease remains a leading cause of mortality worldwide, imposing a substantial burden on global healthcare systems. Early detection and accurate mortality risk assessment are critical for timely intervention and improved patient outcomes. However, existing predictive models often suffer from limited clinical applicability due to their restriction to single diagnostic tasks, insufficient prediction accuracy, and a tendency toward overfitting. METHODS: This study proposes a novel hybrid intelligent model, termed QSAO-BP, which integrates a quartile based Snow Ablation

AI-Powered Structuring of Clinical Information in Parkinson's Disease: An Approach Based on Large Language Models.

Advanced Parkinson's Disease (APD) is clinically heterogeneous, and many APD-defining features are recorded predominantly in unstructured neurology narratives, limiting scalable phenotyping and longitudinal analyses. An approach based on large language models (LLMs) was designed to transform free text from electronic health records (EHRs) into an APD-oriented dataset aligned with Questionnaire for the Evaluation of Advanced Parkinson's Disease (CDEPA)-derived domains. A retrospective EHR cohort identified between January 2018 and January 2023 from a tertiary neurology service comprised 1,201 c

OculoGen: Personalized Text-to-Eye-Movement Video Generation via Cascaded Event-Guided Diffusion.

Text-to-video models struggle to follow oculomotor instructions while preserving the anatomy of a reference eye. We present OculoGen, a cascaded framework for personalized text-to-eye-movement video generation from a description and a reference image. OculoParse converts the text into ordered events, OculoKine predicts gaze and eyelid-openness trajectories, and OculoSynth renders them with control-guided diffusion. These explicit intermediate representations allow independent supervision and targeted error diagnosis. A retrieval-and-warp Control Adapter aligns annotated exemplars to the target

Measuring what matters- patient and clinician priorities for clinical decision support interactions: a modified Delphi study.

BACKGROUND: Patient-centered clinical decision support (PC CDS) is a spectrum of digital tools intended to support patient and clinician decision making. Patient-clinician interaction is key to PC CDS, and patient-clinician interaction measurement areas assess how PC CDS impacts the care process and experiences a patient has accessing and receiving healthcare. Identifying measures that assess if and how PC CDS influences outcomes is essential to measuring impact, but it is unclear what outcomes patients and physicians consider important. This study aimed to develop a prioritized list of measur