David Stoffel, MD

Artificial Intelligence
Radiology AI: Going beyond the algorithm
RapidAI
AI is revolutionizing the way we deliver healthcare, especially in radiology.
May 29, 2024
Ai
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DL algorithm targeting prostate cancer demonstrates clinical impact
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Per participant, a deep-learning (DL) algorithm demonstrated increased specificity of 64.8% compared with radiologists at 21.3%.
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MRI scans show two areas of clinically significant prostate cancer in a 65-year-old man with a prostate-specific antigen level of 8.23 ng/mL. Images are (A, E) axial T2-weighted images, (B, F) axial diffusion-weighted images obtained with a b value of 1500 sec/mm2, (C, G) axial apparent diffusion coefficient images, and (D, H) lesion masks generated by the deep learning algorithm (DLA) on T2-weighted images. The DLA detected a lesion in the right posteromedial peripheral zone of the apex (D), which was assigned as Prostate Imaging Reporting and Data System (PI-RADS) 4. A radiologist also detected the lesion and assigned it PI-RADS 4 (arrow in A-C). A lesion in the right anterior transition zone (H) was detected by the DLA, which was assigned PI-RADS 4. The radiologist did not detect the lesion (arrow in E-G). Both lesions were confirmed as prostate cancer with Gleason grade group 2. Caption and image courtesy of RSNA.
AI boosts identification of incident vertebral fractures on chest CT
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An AI algorithm that measures 3D whole thoracic vertebral bone mineral density on conventional chest CT can help predict incident vertebral fractures.
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An ensemble model combining clinical variables, O-RADS, and deep learning radiomics can diagnose ovarian tumors.
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(A) SHAP summary plot shows the global contributions of variables to the model predictions. (B-E) Local explanation of the model for a single instance of ovarian endometrioid carcinoma: SHAP waterfall plot (B), ultrasound grayscale image (C), ultrasound color Doppler image (D), and 10×10 histopathological image (E). Images are available for republishing under a Creative Commons license (CC BY-NC-ND 4.0).
AI model aimed at adrenal glands, nodules could reduce workload by 10%
By Liz Carey
A future vision of AI in this context could entail analyzing adrenal nodules across multiple phases, extracting radiomic data, and providing a predictability score, study authors said.
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CNN models help determine best treatment for kids with GI obstructions
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Convolutional neural network (CNN) algorithms show promise for identifying gastrointestinal (GI) obstructions on x-ray in a pediatric population, researchers have reported.
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ECR: Integrating AI into radiology workflows benefits the environment
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Sustainability in radiology is a hot topic at this year's ECR in Vienna, and a February 27 talk focused on "green" radiology using AI technology.
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ECR: Repurposed AI boosts breast cancer detection in diverse patient groups
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Repurposed AI imaging tools can improve breast malignancy detection across diverse patient groups.
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ECR: Generative AI yields potential for assisting radiologists
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Generative AI may have novel applications in the clinic, but using the technology comes with its share of challenges and future directions.
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Marc Kohli, MD, discusses current and potential future uses of generative AI in radiology at ECR 2025 in Vienna, Austria. Behind him is an image created by one such generative AI model, which illustrates humans working alongside AI.
LLM Mistral rivals GPT-4 Turbo for extracting clinical history elements
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Mistral outperformed Llama and GPT-4 Turbo in a real-world application that assessed the completeness of clinical histories accompanying radiology imaging orders from the emergency department.
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AI helps predict kidney function decline in Lu-177 patients
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An open-source AI tool can help predict renal function decline in prostate cancer patients undergoing lutetium-177 (Lu-177) radiopharmaceutical therapy.
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An axial CT image illustrating segmentation of functioning kidney tissue (blue areas), excluding kidney cysts and renal pelvis, using TotalSegmentator. Image and caption courtesy of RSNA.
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The use of AI in thoracic imaging has begun to demonstrate "cumulative evidence of effectiveness," but more testing and research are needed to determine its feasibility for this application.
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T2-weighted MRI shows promise for body composition analysis
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Using deep learning with T2-weighted MR imaging is a feasible way to assess patients' body composition, researchers have reported.
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