While most deep learning approaches are developed for single images, in real‐world applications, images are often obtained as a series to inform decision‐making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. Herein, an approach that seamlessly integrates deep learning and traditional machine learning models is presented, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, the multilevel interconnections across joints and damage types into the machine learning model are integrated and the crossregulation map of joint damages in rheumatoid arthritis is revealed. An interactive preprint version of the article can be found at
The integration of an ingestible dosage form with sensing, actuation, and drug delivery capabilities can enable a broad range of surgical‐free diagnostic and treatment strategies. However, the gastrointestinal (GI) tract is a highly constrained and complex luminal construct that fundamentally limits the size of an ingestible system. Recent advancements in mesoscale magnetic crawlers have demonstrated the ability to effectively traverse complex and confined systems by leveraging magnetic fields to induce contraction and bending‐based locomotion. However, the integration of functional components (e.g., electronics) in the proposed ingestible system remains fundamentally challenging. Herein, the creation of a centralized compartment in a magnetic robot by imparting localized flexibility (MR‐LF) is demonstrated. The centralized compartment enables MR‐LF to be readily integrated with modular functional components and payloads, such as commercial off‐the‐shelf electronics and medication, while preserving its bidirectionality in an ingestible form factor. The ability of MR‐LF to incorporate electronics, perform drug delivery, guide continuum devices such as catheters, and navigate air–water environments in confined lumens is demonstrated. The MR‐LF enables functional integration to create a highly integrated ingestible system that can ultimately address a broad range of unmet clinical needs. An interactive preprint version of the article can be found at
- Award ID(s):
- 1830958
- NSF-PAR ID:
- 10381626
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 4
- Issue:
- 11
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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