Microsatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counte...
In this study, interpretable pathological image analysis strategies are established to help medical experts to identify MSI. The strategies only require ubiquitous hematoxylin and eosin-stained whole-...
The strategies achieve two levels of interpretability. First, the image-level interpretability is achieved by generating localization heat maps of important regions based on deep learning. Second, the...
The developed transparent machine learning pipeline is able to detect MSI efficiently and provide comprehensive clinical insights to pathologists. The comprehensible heat maps and features in the inte...
iRENEX is a software module that incorporates scintigraphic and clinical data to interpret 99m Tc- mercaptoacetyltriglycine (MAG3) diuretic studies and provide reasons for their conclusions. Our objec...
Baseline and furosemide 99m Tc-MAG3 acquisitions of 50 patients with suspected obstruction (mean age ± SD, 58.7 ± 15.8 years, 60% female) were randomly selected from an archived database and independe...
The CCC among experts was higher than that among residents, 0.84, versus 0.39, respectively, P < 0.001. When residents reinterpreted the studies with iRENEX, their CCC improved from 0.39 to 0.73, P < ...
iRENEX interpretations were comparable to those of experts. iRENEX reduced interobserver variability among experienced residents and led to better agreement between resident and expert interpretations...
Large language models (LLMs) have shown potential in radiology, but their ability to aid radiologists in interpreting imaging studies remains unexplored. We investigated the effects of a state-of-the-...
In this retrospective study, six radiologists of different experience levels read 40 selected radiographic [n = 10], CT [n = 10], MRI [n = 10], and angiographic [n = 10] studies unassisted (session on...
When assessing if the correct diagnosis was among the top-3 differential diagnoses, diagnostic accuracy improved slightly from 181/240 (75.4%, unassisted) to 188/240 (78.3%, AI-assisted). Similar impr...
Integrating GPT-4 in the diagnostic process improved diagnostic accuracy slightly and diagnostic confidence significantly. Potentially harmful hallucinations and misinterpretations call for caution an...
Using GPT-4 as a virtual assistant when reading images made six radiologists of different experience levels feel more confident and provide more accurate diagnoses; yet, GPT-4 gave factually incorrect...
Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomogra...
Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tiss...
In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from W...
We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals ...
The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE ...
Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent...
Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subseque...
The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlig...
Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectivene...
NCT04906018....
The Japanese Ministry of Health, Labour and Welfare announced about the expansion of duties by the radiological technologists in team medical care in April, 2010, and the importance of image interpret...
Registration of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging as rapid intensity changes caused by a contrast agent lead to large registration errors. To address this p...
The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imag...
This work presents a novel platform for stereo reconstruction in anterior segment ophthalmic surgery to enable enhanced scene understanding, especially depth perception, for advanced computer-assisted...
The proposed platform for stereo reconstruction uses a two-step approach: generating a sparse 3D point cloud from microscopic images, deriving a dense 3D representation by fitting surfaces onto the po...
The accuracy of 3D reconstruction is evaluated on stereo microscopic images of ex vivo porcine eyes, rigid phantom eyes, and synthetic photo-realistic images. The results demonstrate the potential of ...
This work marks a significant advancement in stereo reconstruction for ophthalmic surgery by addressing corneal distortions, a previously often overlooked aspect in such surgical scenarios. This could...
