Dermatology

Automatic Extraction of PET RANO Criteria with an Externally Validated Deep Learning Model: Application to [18F]FDOPA PET Imaging.

BACKGROUND: Automatic segmentation of gliomas on amino acid PET is essential for quantitative tumor assessment, a pillar in monitoring gliomas under treatment. This study aimed to develop a deep learning model for the automated extraction of PET RANO criteria from [18F]FDOPA PET, with external validation. METHODS: A total of 635 static [18F]FDOPA PET scans from three European centers were retrospe...

Artificial intelligence in retinoblastoma and uveal melanoma: a narrative review.

INTRODUCTION: Artificial intelligence (AI) is reshaping diagnostic paradigms across oncology. In ophthalmic oncology encompassing conditions like retinoblastoma and uveal melanoma, AI has immense potential due to the specialty's reliance on advanced imaging and the importance of early and accurate diagnosis. AREAS COVERED: This review explores recent developments in AI applications for ophthalmic ...

Multidimensional immune ecological subtyping identifies RUNX1 as a prognostic factor in uveal melanoma.

Uveal melanoma (UVM) is an aggressive intraocular malignancy with a high risk of metastasis but few effective therapies. However, current molecular cl...

Non-invasive profiling of the tumour microenvironment with spatial ecotypes.

Multicellular programs in the tumour microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and p...

Incorporating functional soft tissue deformations in AI model training for spatially accurate prostate cancer detection.

PURPOSE: To increase performance and generalization ability of artificial intelligence prostate cancer detection systems by simulating physiological s...

Examination of Pathologist-Artificial Intelligence Interactions and Their Impact on Pathologist Accuracy Using Artificial Intelligence-Assisted Scoring of Immunohistochemistry for Human Epidermal Growth Factor Receptor 2.

CONTEXT.—: Advances in computer vision have fueled the development of artificial intelligence (AI)-based algorithms for pathology. AI-assisted approac...

A framework for quantifying and leveraging uncertainty in pre-trained CT denoising model.

OBJECTIVE: To develop an architecture-agnostic framework that estimates, calibrates, and leverages total uncertainty (aleatoric + epistemic) in pre-tr...

Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.

BACKGROUND: Artificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation ...

Overcoming measurement challenges in clinical practice: a deep learning-based approach to monocular surface area measurement.

BACKGROUND: Precise area measurement of body surfaces is essential in clinical trials and clinical practice, particularly for the clinician, to monito...

Automated Measurement of Depigmentation Extent with a New AI Tool Applied to the Example of Vitiligo.

INTRODUCTION: We have developed a digital algorithm to assess skin pigmentation, specifically an artificial intelligence-based image analysis tool tha...

Modelling approaches for predicting the distribution of skin NTDs: A systematic review.

BACKGROUND: Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions ...

Classifying and visualizing medication use in the ABCD study.

Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...

Imaging Biomarkers for Early Differentiation of Candida and Aspergillus Endogenous Fungal Endophthalmitis: Multicenter Optical Coherence Tomography-Based Analysis.

PURPOSE: Endogenous fungal endophthalmitis (EFE) is a rare, sight-threatening intraocular infection with heterogeneous clinical presentations. Candida...

Ultra-low-dose CT for malignant metastasis screening using a deep learning image reconstruction algorithm.

PURPOSE: To evaluate the feasibility and diagnostic performance of ultra-low-dose CT (ULD-CT) for screening malignant metastasis using super-resolutio...

No modality left behind: Adapting to missing modalities via knowledge distillation for brain tumor segmentation.

Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its abili...

AI-Powered Gradient Echo Plural Contrast Imaging (AI-GEPCI)-A Comprehensive Neurological Protocol From a Single MRI Scan.

BACKGROUND: MRI is essential for diagnosing and monitoring neurological diseases. Conventional protocols require multiple sequences to obtain compleme...

Breast ultrasound images for segmentation and classification using multi-task U-Net.

Breast ultrasound imaging is widely used for the early detection of breast cancer due to its accessibility and effectiveness, particularly in dense br...

LDDHybridNet: an ROI-aware CNN-LSTM hybrid framework for accurate and early leaf disease detection in precision agriculture.

Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food se...

Artificial Intelligence-Based Identification of Common Canine Skin Lesions From Clinical Images.

BACKGROUND: Accurate evaluation of skin lesions is an essential component of dermatological examination, yet it can be time-consuming and subject to i...

Overexpression of NMNAT3 suppresses melanoma progression by reprogramming NAD⁺ metabolism.

BACKGROUND: Melanoma represents a highly aggressive and metastatic form of malignant skin cancer. that remains challenging to treat clinically. Tumor ...