Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging

PURPOSE: Purpose Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and focus on thorough network evaluation. APPROACH: We propose a semi-supervised method that leverages data augmentation, consistency regularization, and pseudolabeling and train four cardiac magnetic resonance (MR) segmentation networks. We evaluate the models on multiinstitutional, multiscanner, multidisease cardiac MR datasets using five cardiac functional biomarkers, which are compared to an expert’s measurements using Lin’s concordance correlation coefficient (CCC), the within-subject coefficient of variation (CV), and the Dice coefficient. RESULTS: The semi-supervised networks achieve strong agreement using Lin’s CCC (>0.8), CV similar to an expert, and strong generalization performance. We compare the error modes of the semi-supervised networks against fully supervised networks. We evaluate semi-supervised model performance as a function of labeled training data and with different types of model supervision, showing that a model trained with 100 labeled image slices can achieve a Dice coefficient within 1.10% of a network trained with 16,000+ labeled image slices. CONCLUSION: We evaluate semi-supervision for medical image segmentation using heterogeneous datasets and clinical metrics. As methods for training models with little labeled data become more common, knowledge about how they perform on clinical tasks, how they fail, and how they perform with different amounts of labeled data is useful to model developers and users

The LOINC RSNA radiology playbook - a unified terminology for radiology procedures

Objective: This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced. Methods: The Regenstrief Institute and the Radiological Society of North America (RSNA) developed a unification plan consisting of six objectives 1) develop a unified model for radiology procedure names that represents the attributes with an extensible set of values, 2) transform existing LOINC procedure codes into the unified model representation, 3) create a mapping between all the attribute values used in the unified model as coded in LOINC (ie, LOINC Parts) and their equivalent concepts in RadLex, 4) create a mapping between the existing procedure codes in the RadLex Core Playbook and the corresponding codes in LOINC, 5) develop a single integrated governance process for managing the unified terminology, and 6) publicly distribute the terminology artifacts. Results: We developed a unified model and instantiated it in a new LOINC release artifact that contains the LOINC codes and display name (ie LONG_COMMON_NAME) for each procedure, mappings between LOINC and the RSNA Playbook at the procedure code level, and connections between procedure terms and their attribute values that are expressed as LOINC Parts and RadLex IDs. We transformed all the existing LOINC content into the new model and publicly distributed it in standard releases. The organizations have also developed a joint governance process for ongoing maintenance of the terminology. Conclusions: The LOINC/RSNA Radiology Playbook provides a universal terminology standard for radiology orders and results

Structured reporting: if, why, when, how—and at what expense? Results of a focus group meeting of radiology professionals from eight countries

Purpose: To determine why, despite growing evidence that radiologists and referring physicians prefer structured reporting (SR) to free text (FT) reporting, SR has not been widely adopted in most radiology departments. Methods: A focus group was formed consisting of 11 radiology professionals from eight countries. Eight topics were submitted for discussion. The meeting was videotaped, transcribed, and analyzed according to the principles of qualitative healthcare research. Results: Perceived advantages of SR were facilitation of research, easy comparison, discouragement of ambiguous reports, embedded links to images, highlighting important findings, not having to dictate text nobody will read, and automatic translation of teleradiology reports. Being compelled to report within a rigid frame was judged unacceptable. Personal convictions appeared to have high emotional value. It was felt that other healthcare stakeholders would impose SR without regard to what radiologists thought of it. If the industry were to provide ready-made templates for selected examinations, most radiologists would use them. Conclusion: If radiologists can be convinced of the advantages of SR and the risks associated with failing to participate actively in its implementation, they will take a positive stand. The industry should propose technology allowing SR without compromising accuracy, completeness, workflows, and cost-benefit balance

The Effectiveness of Family Constellation Therapy in Improving Mental Health:A Systematic ReviewPalabras clave(sic)(sic)(sic)

Family/systemic constellation therapy is a short-term group intervention aiming to help clients better understand and then change their conflictive experiences within a social system (e.g., family). The aim of the present systematic review was to synthetize the empirical evidence on the tolerability and effectiveness of this intervention in improving mental health. The PsycINFO, Embase, MEDLINE, ISI Web of Science, Psyndex, PsycEXTRA, ProQuest Dissertations & Theses, The Cochrane Library, Google Scholar, and an intervention-specific organization's databases were searched for quantitative, prospective studies published in English, German, Spanish, French, Dutch or Hungarian up until April 2020. Out of 4,197 identified records, 67 were assessed for eligibility, with 12 studies fulfilling inclusion criteria (10 independent samples; altogether 568 participants). Outcome variables were diverse ranging from positive self-image through psychopathology to perceived quality of family relationships. Out of the 12 studies, nine showed statistically significant improvement postintervention. The studies showing no significant treatment benefit were of lower methodological quality. The random-effect meta-analysis-conducted on five studies in relation to general psychopathology-indicated a moderate effect (Hedges' g of 0.531, CI: 0.387-0.676). Authors of seven studies also investigated potential iatrogenic effects and four studies reported minor or moderate negative effects in a small proportion (5-8%) of participants that potentially could have been linked to the intervention. The data accumulated to date point into the direction that family constellation therapy is an effective intervention with significant mental health benefits in the general population; however, the quantity and overall quality of the evidence is low

Protégé: A Tool for Managing and Using Terminology in Radiology Applications

The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé (http://protege.stanford.edu) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé’s features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology

Technology Acceptance of Augmented Reality and Wearable Technologies

Augmented Reality and Wearables are the recent media and computing technologies, similar, but different from established technologies, even mobile computing and virtual reality. Numerous proposals for measuring technology acceptance exist, but have not been applied, nor fine-tuned to such new technology so far. Within this contribution, we enhance these existing instruments with the special needs required for measuring technology acceptance of Augmented Reality and Wearable Technologies and we validate the new instrument with participants from three pilot areas in industry, namely aviation, medicine, and space. Findings of such baseline indicate that respondents in these pilot areas generally enjoy and look forward to using these technologies, for being intuitive and easy to learn to use. The respondents currently do not receive much support, but like working with them without feeling addicted. The technologies are still seen as forerunner tools, with some fear of problems of integration with existing systems or vendor-lock. Privacy and security aspects surprisingly seem not to matter, possibly overshadowed by expected productivity increase, increase in precision, and better feedback on task completion. More participants have experience with AR than not, but only few on a regular basis.WEKIT (grant agreement no. 687669

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases

Ophthalmic wearable devices for color blindness management

Color vision deficiency (CVD) or color blindness is an ocular disorder that hinders the patients from distinguishing shades of certain colors. Color blind patients are often not considered for critical occupations (e.g., military, police) and cannot differentiate colors in public places or media (i.e., watching TV). The most common form of color blindness is red-green, which is a result of either a missing or defective red or green photoreceptor cone. Since no cure for this disorder exists, sufferers opt for methods to enhance their color perception. The products and methods that have been developed to aid CVD patients are discussed. These technologies include contemporary work on gene therapy, tinted glasses, lenses, optoelectronic glasses, and advanced features developed on smartphones and computers. Among these wearables, tinted glasses, developed by companies such as Enchroma, are the most widely used by CVD patients

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI