Consolidating the ImageCLEF Medical Task Test Collection: 2005-2007 *

The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) has been to improve understanding and system capability in search for medical images. This has been done by developing a test collection that allows system-oriented evaluation of medical image retrieval systems. From 2005-2007, test collections were developed and used for ImageCLEFmed. This paper describes our recent work consolidating the test collections into a single unified collection of 66,662 images and their annotations; 85 topics classified by amenability to visual, textual, or mixed retrieval methods; and relevance judgments. This will provide a comprehensive test collection for further testing of systems and algorithms in medical image retrieval..

A Pilot Prospective Feasibility Study of Organ-at-Risk Definition using Target Contour Testing/Instructional Computer Software (TaCTICS), a Training and Evaluation Platform for Radiotherapy Target Delineation.

Target volume delineation is a critical, but time-consuming step in the creation of radiation therapy plans used in the treatment of many types of cancer. However, variability in target volume definitions can introduce substantial differences in resulting doses to tumors and critical structures. We developed TaCTICS, a web-based educational training software application targeted towards non-expert users. We report on a small, prospective study to evaluate the utility of this online tool in improving conformance of regions-of-interest (ROIs) with a reference set. Eight residents contoured a set of structures for a headand-neck cancer case. Subsequently, they were provided access to TaCTICS as well as contouring atlases to allow evaluation of their contours in reference to other users as well as reference ROIs. The residents then contoured a second case using these resources. Volume overlap metrics between the users showed a substantial improvement following the intervention. Additionally, 66 % of users reported that they found TaCTICS to be a useful educational tool and all participants reported they would like to use TaCTICS to track their contouring skills over the course of their residency

Radiomics of Lung Nodules: A Multi- Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level cooccurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of Ն0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy

Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity

Purpose: Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP. Design: Retrospective cohort study. Participants: Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022. Methods: Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO2). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models. Main Outcome Measures: Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score. Results: We found that both daily average and COV of FiO2 were associated with more severe ROP (adjusted P < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04). Conclusions: The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology

The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article