Peter Krawitz | Search PubMed

PubMed2026

InsightRP2-An Interdisciplinary Approach Toward Therapy Development in RP2-Associated Retinopathy.

Advancing the development of specific gene therapies for rare genetic eye disorders is a major challenge in modern translational vision research. It requires integrated clinical, molecular, mechanistic, and regulatory expertise, yet these aspects are often addressed in isolation. Combining complementary skills, knowledge, and expertise within an integrated, highly interactive research framework has the capacity to drive and advance therapeutic development. Moreover, early-stage involvement of patients and targeted clinical observation, alongside molecular and therapeutic research, potentially enable clinical trial readiness and support the translation of preclinical therapeutic innovations into clinical care. In this Perspective article, we present the InsightRP2 framework, an integrated translational strategy that incorporates clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design with the aim to facilitate the development of a targeted gene therapy for RP2-associated retinitis pigmentosa.

AAV↗

PubMed2026

Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation.

Evaluating hand and wrist radiographs is essential in pediatric endocrinology and clinical genetics, particularly for the assessment of suspected skeletal anomalies. In this study, we present Auto-Bone-Caliper, an automated system for the segmentation and length measurement of metacarpal and phalangeal (M&P) bones, trained and evaluated on public datasets comprising both normal and dysmorphic cases. We first introduce InstanceSAM, a two-stage framework that detects and segments all 19 M&P bones in pediatric hand radiographs, achieving Dice scores of 98.7% for normal bones and 95.0% for dysmorphic bones. We further develop and evaluate three methods for bone-length estimation, identifying a k-means-based approach as the most accurate, with relative errors of 2.2% for normal bones and 4.5% for dysmorphic bones. Our automated pipeline, Auto-Bone-Caliper, integrates InstanceSAM with the k-means-based length-estimation method. To enable scale-independent downstream analyses, we derive relative bone-length measures from the automated measurements. Using these relative measures, we statistically compare measurements obtained using Auto-Bone-Caliper on an independent dataset with a healthy reference catalog of normal bone morphologies, observing a high level of agreement (Wasserstein-1 distance = 0.012). Finally, we demonstrate a potential clinical use case of Auto-Bone-Caliper by obtaining relative metacarpophalangeal pattern profiles for three genetic conditions, namely Turner syndrome, achondroplasia, and pseudohypoparathyroidism. Our results highlight the potential of the Auto-Bone-Caliper to streamline and standardize M&P length measurement, providing an objective and reproducible tool suitable for clinical application.

Humans↗