FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks

The US Food and Drug Administration is clearing an increasing number of artificial intelligence and machine learning (AI/ML)-based medical devices through the 510(k) pathway. This pathway allows clearance if the device is substantially equivalent to a former cleared device (ie, predicate). We analysed the predicate networks of cleared AI/ML-based medical devices (cleared between 2019 and 2021), their underlying tasks, and recalls. More than a third of cleared AI/ML-based medical devices originated from non-AI/ML-based medical devices in the first generation. Devices with the longest time since the last predicate device with an AI/ML component were haematology (2001), radiology (2001), and cardiovascular devices (2008). Especially for devices in radiology, the AI/ML tasks changed frequently along the device's predicate network, raising safety concerns. To date, only a few recalls might have affected the AI/ML components. To improve patient care, a stronger focus should be placed on the distinctive characteristics of AI/ML when defining substantial equivalence between a new AI/ML-based medical device and predicate devices

Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains

Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches. Although such models depict excellent generative capabilities, they do not typically generalize well to specific domains such as medical images that have fundamentally shifted distributions compared to natural images. Building generative models for medical images that faithfully depict clinical context may help alleviate the paucity of healthcare datasets. Thus, in this study, we seek to research and expand the representational capabilities of large pretrained foundation models to medical concepts, specifically for leveraging the Stable Diffusion model to generate domain specific images found in medical imaging. We explore the sub-components of the Stable Diffusion pipeline (the variational autoencoder, the U-Net and the text-encoder) to fine-tune the model to generate medical images. We benchmark the efficacy of these efforts using quantitative image quality metrics and qualitative radiologist-driven evaluations that accurately represent the clinical content of conditional text prompts. Our best-performing model improves upon the stable diffusion baseline and can be conditioned to insert a realistic-looking abnormality on a synthetic radiology image, while maintaining a 95% accuracy on a classifier trained to detect the abnormality.Comment: 17 pages, 8 figure

Pairwise interactions in tropical ant communities

Ecological communities are structured by competitive, predatory, mutualistic and parasitic interactions combined with chance events. Separating deterministic from stochastic processes is possible, but finding statistical evidence for specific biological interactions is challenging. We attempt to solve this problem for ant communities nesting in epiphytic bird’s nest ferns (Asplenium nidus) in Borneo’s lowland rainforest. By recording the frequencies with which each and every single ant species occurred together, we were able to test statistically for patterns associated with interspecific competition. We found evidence for competition, but the resulting co-occurrence pattern was the opposite of what we expected. Rather than detecting species segregation—the classical hallmark of competition—we found species aggregation. Moreover, our approach of testing individual pairwise interactions mostly revealed spatially positive rather than negative associations. Significant negative interactions were only detected among large ants, and among species of the subfamily Ponerinae. Remarkably, the results from this study, and from a corroborating analysis of ant communities known to be structured by competition, suggest that competition within the ants leads to species aggregation rather than segregation. We believe this unexpected result is linked with the displacement of species following asymmetric competition. We conclude that analysing co-occurrence frequencies across complete species assemblages, separately for each species, and for each unique pairwise combination of species, represents a subtle yet powerful way of detecting structure and compartmentalisation in ecological communities.We acknowledge support from the University of Cambridge, NERC, The Royal Society South East Asia Rainforest Research Programme, Yayasan Sabah, Danum Valley Management Committee, and the Economic Planning Unit in Kuala Lumpur. TMF was supported by the Czech Science Foundation (14-32302S, 16-09427S), and the Australian Research Council (DP140101541).This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Elsevie

Fully automated breast segmentation on spiral breast computed tomography images

INTRODUCTION The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required. We propose a fully automated breast segmentation method for breast CT images. METHODS The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five-point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists. RESULTS The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90-0.97 and in DCs 0.01-0.08. The readers rated 4.5-4.8 (5 highest score) with an excellent inter-reader agreement. The breast density varied by 3.7%-7.1% when including mis-segmented muscle or skin. CONCLUSION The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk

Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification

Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans and proceeding to ILD classification using "patch montages". Furthermore, we investigate how domain adaptive pretraining (DAPT) CLIP with task-specific images (CT "patch montages" extracted with ILD-specific prompts for CLIP) and/or text (lung-specific sections of radiology reports) affects downstream ILD classification performance. By leveraging CLIP-extracted "patch montages" and DAPT, we achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data. This work highlights the versatility and potential of multimodal models like CLIP for medical image classification tasks where labeled data is scarce.Comment: 11 pages, 11 figure

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, and clinical text) and via prompting (zero-shot, in-context learning) or parameter-efficient fine-tuning (prefix tuning, LoRA). Our results on the MIMIC-III dataset consistently demonstrate best performance by maximally adapting to the task via pretraining on clinical text and parameter-efficient fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.Comment: 12 pages, 9 figure

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure

Bacterial computing with engineered populations

We describe strategies for the construction of bacterial computing platforms by describing a number of results from the recently completed bacterial computing with engineered populations project. In general, the implementation of such systems requires a framework containing various components such as intracellular circuits, single cell input/output and cell–cell interfacing, as well as extensive analysis. In this overview paper, we describe our approach to each of these, and suggest possible areas for future research

Reflection of neuroblastoma intratumor heterogeneity in the new OHC-NB1 disease model

Accurate modeling of intratumor heterogeneity presents a bottleneck against drug testing. Flexibility in a preclinical platform is also desirable to support assessment of different endpoints. We established the model system, OHC-NB1, from a bone marrow metastasis from a patient diagnosed with MYCN-amplified neuroblastoma and performed whole-exome sequencing on the source metastasis and the different models and passages during model development (monolayer cell line, 3D spheroid culture and subcutaneous xenograft tumors propagated in mice). OHC-NB1 harbors a MYCN amplification in double minutes, 1p deletion, 17q gain and diploid karyotype, which persisted in all models. A total of 80-540 single-nucleotide variants (SNVs) was detected in each sample, and comparisons between the source metastasis and models identified 34 of 80 somatic SNVs to be propagated in the models. Clonal reconstruction using the combined copy number and SNV data revealed marked clonal heterogeneity in the originating metastasis, with 4 clones being reflected in the model systems. The set of OHC-NB1 models represents 43% of somatic SNVs and 23% of the cellularity in the originating metastasis with varying clonal compositions, indicating that heterogeneity is partially preserved in our model system

Pollinator conservation: the difference between managing for pollination services and preserving pollinator diversity

Our review looks at pollinator conservation and highlights the differences in approach between managing for pollination services and preserving pollinator diversity. We argue that ecosystem service management does not equal biodiversity conservation, and that maintaining species diversity is crucial in providing ecosystem resilience in the face of future environmental change. Management and policy measures therefore need to focus on species not just in human dominated landscapes but need to benefit wider diversity of species including those in specialised habitats. We argue that only by adopting a holistic ecosystem approach we can ensure the conservation and sustainable use of biodiversity and ecosystem services in the long-term