Machine Learning-assisted directed evolution (MLDE) is a powerful tool for efficiently navigating antibody fitness landscapes. Many structure-aware MLDE pipelines rely on a single conformation or a single committee across all conformations, limiting their ability to separate conformational uncertainty from epistemic uncertainty. Here, we introduce a rank -conditioned committee (RCC) framework that leverages ranked conformations to assign a deep neural network committee per rank. This design enables a principled separation between epistemic uncertainty and conformational uncertainty. We validate our RCC-MLDE approach on SARS-CoV-2 antibody docking, demonstrating significant improvements over baseline strategies. Our results offer a scalable route for therapeutic antibody discovery while directly addressing the challenge of modeling conformational uncertainty.

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles - the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables Threads to capture the tissue's underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction, and survival prediction, Threads outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community.

Fiducial marker positions in projection image of cone-beam computed tomography (CBCT) scans have been studied to evaluate daily residual motion during breath-hold radiation therapy. Fiducial marker migration posed challenges in accurately locating markers, prompting the development of a novel algorithm that reconstructs volumetric probability maps of marker locations from filtered gradient maps of projections. This guides the development of a Python-based algorithm to detect fiducial markers in projection images using Meta AI's Segment Anything Model 2 (SAM 2). Retrospective data from a pancreatic cancer patient with two fiducial markers were analyzed. The three-dimensional (3D) marker positions from simulation computed tomography (CT) were compared to those reconstructed from CBCT images, revealing a decrease in relative distances between markers over time. Fiducial markers were successfully detected in 2777 out of 2786 projection frames. The average standard deviation of superior-inferior (SI) marker positions was 0.56 mm per breath-hold, with differences in average SI positions between two breath-holds in the same scan reaching up to 5.2 mm, and a gap of up to 7.3 mm between the end of the first and beginning of the second breath-hold. 3D marker positions were calculated using projection positions and confirmed marker migration. This method effectively calculates marker probability volume and enables accurate fiducial marker tracking during treatment without requiring any specialized equipment, additional radiation doses, or manual initialization and labeling. It has significant potential for automatically assessing daily residual motion to adjust planning margins, functioning as an adaptive radiation therapy tool.

Arman Rahmim Departments of Radiology and Physics, University of British Columbia Tyler J. Bradshaw Department of Radiology, University of Wisconsin Guido Davidzon Department of Radiology, Division of Nuclear Medicine & Molecular Imaging, Stanford University Joyita Dutta Department of Biomedical Engineering, University of Massachusetts Amherst Georges El Fakhri PET Center, Departments of Radiology & Biomedical Engineering and Bioinformatics & Data Science, Yale University Munir Ghesani United Theranostics Nicolas A. Karakatsanis Department of Radiology, Weill Cornell Medical College of Cornell University, New York Quanzheng Li Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School Chi Liu Department of Radiology and Biomedical Imaging, Yale University Emilie Roncali Departments of Biomedical Engineering and Radiology, University of California, Davis Babak Saboury Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health Tahir Yusufaly Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine Abhinav K. Jha Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis Abstract The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was "AI in Action". Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript. Introduction The Society of Nuclear Medicine & Molecular Imaging (SNMMI) 2nd Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Over 100 community members and stakeholders from academia, healthcare, industry, and NIH gathered to discuss the emerging role of AI in nuclear medicine. It featured two plenaries, panel discussions, talks from leading experts in the field, and was concluded by a round table discussion on key findings, next steps, and call to action.

Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is conventionally categorized into three subtypes identifiable on pathology and on lung computed tomography (CT) images. Recent work has led to the unsupervised learning of ten spatially-informed lung texture patterns (sLTPs) on lung CT, representing distinct patterns of emphysematous lung parenchyma based on both textural appearance and spatial location within the lung, and which aggregate into 6 robust and reproducible CT Emphysema Subtypes (CTES). Existing methods for sLTP segmentation, however, are slow and highly sensitive to changes in CT acquisition protocol. In this work, we present a robust 3-D squeeze-and-excitation CNN for supervised classification of sLTPs and CTES on lung CT. Our results demonstrate that this model achieves accurate and reproducible sLTP segmentation on lung CTscans, across two independent cohorts and independently of scanner manufacturer and model.

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.

Diabetic retinopathy is an ocular condition that affects individuals with diabetes mellitus. It is a common complication of diabetes that can impact the eyes and lead to vision loss. One method for diagnosing diabetic retinopathy is the examination of the fundus of the eye. An ophthalmologist examines the back part of the eye, including the retina, optic nerve, and the blood vessels that supply the retina. In the case of diabetic retinopathy, the blood vessels in the retina deteriorate and can lead to bleeding, swelling, and other changes that affect vision. We proposed a method for detecting diabetic diabetic severity levels. First, a set of data-prerpocessing is applied to available data: adaptive equalisation, color normalisation, Gaussian filter, removal of the optic disc and blood vessels. Second, we perform image segmentation for relevant markers and extract features from the fundus images. Third, we apply an ensemble of classifiers and we assess the trust in the system.

This opinion piece is inspired by the old Danish proverb: "Making predictions is hard, especially about the future" (1). As every reader knows, the momentum of artificial intelligence (AI) and the eventual implementation of deep learning models seem assured. Some pundits have gone considerably further, however, and predicted a sweeping AI takeover of radiology. Although many radiologists support AI and believe it will enable greater efficiency, a recent study of medical students found very different reactions (2). While such doomsday predictions are understandably attention-grabbing, they are highly unlikely, at least in the short term.

When Regina Barzilay had a routine mammogram in her early 40s, the image showed a complex array of white splotches in her breast tissue. The marks could be normal, or they could be cancerous--even the best radiologists often struggle to tell the difference. Her doctors decided the spots were not immediately worrisome. In hindsight, she says, "I already had cancer, and they didn't see it." Over the next two years Barzilay underwent a second mammogram, a breast MRI and a biopsy, all of which continued to yield ambiguous or conflicting findings. Ultimately she was diagnosed with breast cancer in 2014, but the path to that diagnosis had been unbelievably frustrating. "How do you do three tests and get three different results?" she wondered.

The nation's leading association that fights against diabetes released a new set of clinical standards that for the first time include the use of autonomous artificial intelligence (AI). The American Diabetes Association (ADA)'s 2020 Standards of Medical Care in Diabetes states that, "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." To date, IDx-DR is the first and only FDA-authorized autonomous AI diagnostic system for the detection of diabetic retinopathy and macular edema. It is currently in use at a number of large health systems that each serve tens of thousands of people with diabetes and have struggled to implement diabetic retinopathy eye exams at scale for their large diabetes population. "The ADA's inclusion of our technology in its Standards of Care marks a significant move toward mainstream adoption of autonomous AI in clinical care," said Michael Abramoff, MD, PhD, Founder and Executive Chairman at IDx. "Our early customers are visionary leaders who foresaw that autonomous AI would one day become a standard of care for diabetic retinopathy screening, and taking that leap is paying off for them. Already, health systems that are using IDx-DR have experienced significant improvements in accessibility, efficiency and compliance rates, unleashing massive potential for cost savings and improved patient outcomes."