搜索结果：Medico-legal

Legal age estimation plays a critical role in forensic and medico-legal contexts, where decisions must be supported by accurate, robust, and reproducible methods with explicit uncertainty quantification. While prior artificial intelligence (AI)-based approaches have primarily focused on hand radiographs or dental imaging, clavicle computed tomography (CT) scans remain underexplored despite their documented effectiveness for legal age estimation. In this work, we present an interpretable, multi-stage pipeline for legal age estimation from clavicle CT scans. The proposed framework combines (i) a feature-based connected-component method for automatic clavicle detection that requires minimal manual annotation, (ii) an Integrated Gradients-guided slice selection strategy used to construct the input data for a multi-slice convolutional neural network that estimates legal age, and (iii) conformal prediction intervals to support uncertainty-aware decisions in accordance with established international protocols. The pipeline is evaluated on 1,158 full-body post-mortem CT scans from a public forensic dataset (the New Mexico Decedent Image Database). The final model achieves state-of-the-art

Every day, many people die under violent circumstances, whether from crimes, war, migration, or climate disasters. Medico-legal and law enforcement institutions document many portraits of the deceased for evidence, but cannot immediately carry out identification on them. While traditional image editing tools can process these photos for public release, the workflow is lengthy and produces suboptimal results. In this work, we leverage advances in image generation models, which can now produce photorealistic human portraits, to introduce FlowID, an identity-preserving facial reconstruction method. Our approach combines single-image fine-tuning, which adapts the generative model to out-of-distribution injured faces, with attention-based masking that localizes edits to damaged regions while preserving identity-critical features. Together, these components enable the removal of artifacts from violent death while retaining sufficient identity information to support identification. To evaluate our method, we introduce InjuredFaces, a novel benchmark for identity-preserving facial reconstruction under severe facial damage. Beyond serving as an evaluation tool for this work, InjuredFaces pr

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and

Accurate, reproducible burn assessment is critical for treatment planning, healing monitoring, and medico-legal documentation, yet conventional visual inspection and 2D photography are subjective and limited for longitudinal comparison. This paper presents an AI-enabled burn assessment and management platform that integrates multi-view photogrammetry, 3D surface reconstruction, and deep learning-based segmentation within a structured clinical workflow. Using standard multi-angle images from consumer-grade cameras, the system reconstructs patient-specific 3D burn surfaces and maps burn regions onto anatomy to compute objective metrics in real-world units, including surface area, TBSA, depth-related geometric proxies, and volumetric change. Successive reconstructions are spatially aligned to quantify healing progression over time, enabling objective tracking of wound contraction and depth reduction. The platform also supports structured patient intake, guided image capture, 3D analysis and visualization, treatment recommendations, and automated report generation. Simulation-based evaluation demonstrates stable reconstructions, consistent metric computation, and clinically plausible l

Industrial Revolution 4.0 transforms healthcare systems. The first three technological revolutions changed the relationship between human and machine interaction due to the exponential growth of machine numbers. The fourth revolution put humans into a situation where heterogeneous data is produced with unmatched quantity and quality not only by traditional methods, enforced by digitization, but also by ubiquitous computing, machine-to-machine interactions and smart environment. The modern cyber-physical space underlines the role of the person in the expanding context of computerization and big data processing. In healthcare, where data collection and analysis particularly depend on human efforts, the disruptive nature of these developments is evident. Adaptation to this process requires deep scrutiny of the trends and recognition of future medical data technologies` evolution. Significant difficulties arise from discrepancies in requirements by healthcare, administrative and technology stakeholders. Black box and grey box decisions made in medical imaging and diagnostic Decision Support Software are often not transparent enough for the professional, social and medico-legal requirem

The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply

The magnitude of force used in a stabbing incident can be difficult to quantify, although the estimate given by forensic pathologists is often seen as `critical' evidence in medico-legal situations. The main objective of this study is to develop a quantitative measure of the force associated with a knife stabbing biological tissue, using a combined experimental and numerical technique. A series of stab-penetration tests were performed to quantify the force required for a blade to penetrate skin at various speeds and using different `sharp' instruments. A computational model of blade penetration was developed using ABAQUS/EXPLICIT, a non-linear finite element analysis (FEA) commercial package. This model, which incorporated element deletion along with a suitable failure criterion, is capable of systematically quantifying the effect of the many variables affecting a stab event. This quantitative data could, in time, lead to the development of a predictive model that could help indicate the level of force used in a particular stabbing incident.

During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions. Unfortunately, medico-legal and technical hurdles make it difficult to access and query medical images for training. In this paper we train a generative adversarial network (GAN) to synthesize 512 x 512 high-resolution mammograms. The resulting model leads to the unsupervised separation of high-level features (e.g. the standard mammography views and the nature of the breast lesions), with stochastic variation in the generated images (e.g. breast adipose tissue, calcification), enabling user-controlled global and local attribute-editing of the synthesized images. We demonstrate the model's ability to generate anatomically and medically relevant mammograms by achieving an average AUC of 0.54 in a double-blind study on four expert mammography radiologists to distinguish between generated and real images, ascribing to the high visual quality of the synthesized and edited mammograms, and to their potential use in advancing and facilitating medical education.

A new quantum device can generate precisely controlled bursts of sound-like particles, or phonons, by forcing electrons through an ultra-thin crystal at extremely low temperatures。 The surprising behavior pushes beyond the limits predicted by current theories, suggesting scientists need to rethink how energy moves through advanced materials。 In the

A new SETI study suggests we may be overlooking alien signals not because they aren't there, but because their own stars are scrambling them before they escape into space。 Turbulent plasma and powerful stellar storms can spread an ultra-narrow radio transmission across a wider range of frequencies, making it much harder for traditional searches to

Scientists have found that staple-shaped particles can tangle together to create a material that is both strong and flexible。 Unlike conventional materials, these particles can be locked into a sturdy structure or rapidly unraveled using vibrations。 The unusual behavior could open the door to recyclable buildings, reconfigurable structures, and eve

A rare meteorite has revealed evidence of a massive lost world that once orbited the young Sun before being destroyed in a catastrophic collision。 The discovery suggests some early planets formed from dramatically different materials than Earth and Mars, rewriting part of the solar system’s origin story

NASA’s upgraded Cold Atom Lab is turning the International Space Station into a frontier for quantum research, creating ultra-cold matter that behaves in astonishing ways。 The experiments could unlock new discoveries about the universe while paving the way for powerful future technologies in space and on Earth

What if consciousness isn’t limited to brains like ours。 Philosophers Eric Schwitzgebel and Jeremy Pober argue that consciousness could arise in many different forms of life, even in beings built from radically different materials than those found on Earth。 Drawing on the vastness of the universe and the likely existence of countless alien civiliza

A colossal ancient collision may have left some of the Moon’s deepest secrets surprisingly close to future Artemis landing sites。 By recreating the impact that formed the giant South Pole-Aitken basin—the Moon’s largest and oldest crater—scientists found that a low-angle strike from a large, iron-cored object blasted material from deep inside the M

Walkthrough experience includes visits to stars, exoplanets, and observatories

Doctors find grey fluid and dead, metallic flesh inside poisoned woman's hip

Two newly confirmed "super-puff" planets are so diffuse that they are less dense than cotton candy, despite being about the size of Jupiter。 Their rare orbital relationship and enormous, lightweight atmospheres could provide valuable clues about how some of the strangest planets in the galaxy come to exist

Researchers have created quantum control techniques that can make a system appear to run backward in time。 By precisely managing quantum measurements, they can reshape the system's arrow of time and even harvest energy from the measurement process itself。 The breakthrough could lead to more powerful quantum computers, quantum batteries, and other a