Structure–Function Relationships in Geographic Atrophy Based on Mesopic Microperimetry, Fundus Autofluorescence, and Optical Coherence Tomography

Translational Vision Science & Technology, Journal Year: 2025, Volume and Issue: 14(2), P. 7 - 7

Published: Feb. 5, 2025

Purpose: To examine relationships between retinal structure and visual function in geographic atrophy (GA) by analyzing spatial agreement absolute scotomas macular structure, focusing on (1) choroidal hypertransmission, a key feature of complete pigment epithelium outer (cRORA), (2) fundus autofluorescence (FAF)–defined GA. Methods: Mesopic microperimetry (using novel T-shaped pattern) multimodal imaging were recorded longitudinally phase II GA trial. Horizontal vertical optical coherence tomography (OCT) line scans (corresponding to the T axes) graded for hypertransmission; FAF images Spatial concordance zones scotoma was quantified Dice similarity coefficient (DSC). Results: The analysis population comprised 24 participants (mean follow-up 26.8 months). For estimated mean DSC 0.70 (95% confidence interval [CI], 0.64–0.77). This significantly higher than FAF-defined (0.67; 95% CI, 0.61–0.74; difference = 0.03, 0.02–0.05, P < 0.001). Mean OCT reflectivity strongly associated with likelihood severity scotoma. Conclusions: structural features is moderately high slightly hypertransmission supports cRORA feature, as an outcome measure interventional trials. provides more information explain alone. However, given some discordance both features, performing alongside remains important. Translational Relevance: These findings provide insights into complex relationship contribute nuanced understanding measures.

Language: Английский

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Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning

Physics and Imaging in Radiation Oncology, Journal Year: 2023, Volume and Issue: 28, P. 100501 - 100501

Published: Oct. 1, 2023

Background and purposeArtificial Intelligence (AI)-based auto-contouring for treatment planning in radiotherapy needs extensive clinical validation, including the impact of editing after automatic segmentation. The aims this study were to assess performance a commercial system Clinical Target Volumes (CTVs) (prostate/seminal vesicles) selected Organs at Risk (OARs) (rectum/bladder/femoral heads+femurs), evaluating also inter-observer variability (manual vs automatic+editing) reduction contouring time.Materials methodsTwo expert observers contoured CTVs/OARs 20 patients our Treatment Planning System (TPS). Computed Tomography (CT) images sent workstation: contours generated back TPS, where could edit them if necessary. Inter- intra-observer consistency was estimated using Dice Similarity Coefficients (DSC). Radiation oncologists asked score quality contours, ranging from 1 (complete re-contouring) 5 (no editing). Contouring times automatic+edit) compared.ResultsDSCs only) consistent with (between 0.65 seminal vesicles 0.94 bladder); further improved performances (range: 0.76-0.94). median 4 (little editing) it <4 3/2 two respectively. Inter-observer automatic+editing significantly, being lower than manual (e.g.: vesicles: 0.83vs0.73; prostate: 0.86vs0.83; rectum: 0.96vs0.81). Oncologist time reduced 17-24 minutes 3-7 (p<0.01).ConclusionAutomatic AI-based followed by can replace contouring, resulting significantly segmentation better between operators.

Language: Английский

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Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis

Physical and Engineering Sciences in Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting evaluating most suitable solution. To support adoption of AI systems, Selection Criteria recommendations were developed enable a holistic evaluation vendors, considering not only raw performance but associated risks uniquely related deployment AI. In-house experience key bodies work on ethics, standards, best practices for in Radiation Oncology reviewed inform selection criteria strategies. A retrospective analysis using was performed across six including quantitative assessment five metrics (Dice, Hausdorff Distance, Average Surface Dice, Added Path Length) 20 head neck, thoracic, 19 male pelvis patients models as March 2023. total 47 identified seven categories. showed that overall no vendor exceedingly well, with systematically poor Data Security & Responsibility, Vendor Support Tools, Transparency Ethics. In terms performance, vendors varied widely from excellent poor. As new regulations come into force scope systems adapt needs, continued interest ensuring safe, fair, transparent will persist. The framework provided herein aims promote user confidence by exploring breadth clinically relevant factors informed decision-making.

Language: Английский

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Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 241 - 249

Published: Jan. 1, 2025

Language: Английский

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Geometric and dosimetric evaluation of a commercial AI auto‐contouring tool on multiple anatomical sites in CT scans

Journal of Applied Clinical Medical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There a need for more efficient consistent methods. This study evaluated the performance Varian Ethos AI auto‐contouring tool assess its potential integration into clinical workflows. retrospective included 223 patients with treatment sites in pelvis, abdomen, thorax, head neck regions. The generated auto‐contours each patients’ pre‐treatment planning CT, 45 unique structures were across cohort. Multiple measures geometric similarity computed, including surface Dice Similarity Coefficient (sDSC) mean distance agreement (MDA). Dosimetric concordance was by comparing dose maximum 2 cm 3 (D cc ) between contours. demonstrated high accuracy well‐defined like bladder, lungs, femoral heads. Smaller those less defined boundaries, such as optic nerves duodenum, showed lower agreement. Over 70% sDSC > 0.8, 74% had MDA < 2.5 mm. Geometric generally correlated dosimetric concordance, however differences contour definitions did result some exhibiting deviations. offers promising reliability many anatomical structures, supporting use Auto‐contouring errors, although rare, highlight importance ongoing QA expert oversight.

Language: Английский

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Proposal for a Method for Assessing the Quality of an Updated Deep Learning-Based Automatic Segmentation Program

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

This study aimed to verify whether a commercial deep learning-based automatic segmentation (DLS) method can maintain contour geometric accuracy post-update and propose streamlined validation that minimizes the burden on clinical workflows. included 109 participants. Radiation oncologists used computed tomography (CT) imaging identify 28 organs located in head neck, chest, abdomen, pelvic regions. Contours were delineated CT images using AI-Rad Companion Organs RT (AIRC; Siemens Healthineers, Erlangen, Germany) versions VA30, VA50, VA50. The Dice similarity coefficient, maximum Hausdorff distance, mean distance agreement calculated contours with significant differences among versions. To evaluate identified contours, ground truth was defined as by radiation oncologists, indices for VA60 recalculated. Statistical analysis performed between each version. Among evaluated, nine did not satisfy established criteria. revealed brain, rectum, bladder differed substantially across AIRC In particular, pre-update rectum had (range) of 0.76 (0.40-1.16), whereas exhibited lower quality, 1.13 (0.24-5.68). Therefore, DLS methods undergo regular updates must be reassessed quality region interest. proposed help reduce workflows while appropriately evaluating performance.

Language: Английский

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The Worrisome Impact of an Inter-rater Bias on Neural Network Training

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 463 - 473

Published: Jan. 1, 2025

Language: Английский

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Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries

Strahlentherapie und Onkologie, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

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Geometric and dosimetric impact of inter-professional contour variability in the upper abdomen for MR-linac

Journal of medical imaging and radiation sciences, Journal Year: 2025, Volume and Issue: 56(5), P. 101980 - 101980

Published: May 13, 2025

Language: Английский

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ESR Essentials: a step-by-step guide of segmentation for radiologists—practice recommendations by the European Society of Medical Imaging Informatics

European Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: May 22, 2025

Abstract High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in future. As medical imaging advances, accurately segmenting anatomical pathological structures increasingly used obtain quantitative data valuable insights. Segmentation volumetric analysis could enable precise diagnosis, treatment planning, patient monitoring. These guidelines aim improve accuracy consistency, allowing better decision-making both environments. Practical advice on planning organization provided, focusing quality, precision, communication among teams. Additionally, tips strategies improving practices radiology radiation oncology are discussed, as pitfalls avoid. Key Points AI continues advance, volumetry will become integrated into making it essential radiologists stay informed about its applications diagnosis . There a significant lack of practical resources tailored specifically technical topics like Establishing clear rules best can streamline assessment settings, easier manage leading accurate care

Language: Английский

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