Showing 5 Abstracts.
08 : 40 AM
Assessment of Classic Metaphyseal Lesions on Bone Surveys for Non-Accidental Trauma with Deep Learning Algorithms
Xue Christine, Nowrangi Rajeev, Smith John, Acharya Patricia
Final Pr. ID: Paper #: 021
To assess whether a convolutional neural network (CNN) can be trained via transfer learning to accurately diagnose metaphyseal corner fractures on long bone radiographs by comparing its performance to that of a board-certified pediatric radiologist. Secondary endpoints will include the ability of the CNN to detect the type of long bone presented on the radiograph as well as identify the chronicity of the fracture. Read More
Authors: Xue Christine , Nowrangi Rajeev , Smith John , Acharya Patricia
Keywords: AI, Nonaccidental Trauma, metaphyseal corner fractures
08 : 30 AM
Assessment of Germinal Matrix Hemorrhage on Cranial Ultrasound with Deep Learning Algorithms
Kim Kevin, Nowrangi Rajeev, Mcgehee Arianna, Joshi Neil, Acharya Patricia
Final Pr. ID: Paper #: 020
To assess whether a convolutional neural network (CNN) can be trained via transfer learning to accurately diagnose germinal matrix hemorrhage-intraventricular hemorrhage (GMH-IVH) on cranial ultrasound (CUS) by comparing its performance to that of a board-certified radiologist. Secondary endpoints will include the ability of CNN to grade GMH-IVH severity, identify GMH-IVH on MRI, and assess low-grade GMH-IVH on CUS that cannot reliably be seen by a radiologist. Read More
Authors: Kim Kevin , Nowrangi Rajeev , Mcgehee Arianna , Joshi Neil , Acharya Patricia
Keywords: AI, Cranial ultrasound, germinal matrix hemorrhage
Desai Sudhen, Steve Mccaulley, Vaidya Vinay
Final Pr. ID: Poster #: EDU-041
Patients requiring enteral support typically have not had a primary provider for the maintenance of their enteral tubes and at many institutions are only seen on an as-needed basis. Specifically, patients with gastrojejunal feeding tubes tend to present acutely (e.g. tube occlusion, dysfunction or accidental removal) to Interventional Radiology (IR) departments with need for exchange. Standard patient presentation is via the ED or their GI offices after significant time investment on the part of the families and involved providers to arrange the IR visit. Read More
Authors: Desai Sudhen , Steve Mccaulley , Vaidya Vinay
Keywords: Data Management, AI, Gastrojejunal feeding tube
Large Multimodal Models for Pediatric Radiologists: What You Need to Know to Leverage LMMs in Academic and Clinical Settings
Final Pr. ID: Poster #: EDU-004
It hasn’t even been 1 year since chatGPT came out and changed the world. Yet, today, we already have Large Multimodal Models, similar to chatGPT, that can now process images and voice as well.
The capabilities of these models have specifically piqued the interest of Radiologists, who have been exploring use cases in both academic research and clinical practice. Researchers at the NIH have developed a GPT that can process radiology reports to process result findings, locally. This means that all the patient health information stays within your network, and you can use GPTs without compromising HIPAA or safety concerns.
What’s even greater about the local GPT that the NIH researchers have developed, is that there is no need for costly and time-consuming fine-tuning. The performance of the local GPT developed, just by utilizing some techniques, were on par with the state-of-the-art tool developed by Stanford.
The purpose of this educational exhibit is to highlight the capabilities that LMMs have in diagnosing and augmenting the radiologist workflow.
I will discuss how there is no need to become a technical expert to ‘fine tune’ models. I will discuss how you can implement LMMs locally to protect health information. I will highlight the advanced LLM techniques that is used to improve the accuracy of outputted results. I will convey the idea that we need to embrace these technologies that are being improved upon at an exponential rate.
Ultimately, I will tie this in to how pediatric radiologists can use the techniques I talk about to conduct academic research, and will get people to think about how they can utilize their own local GPTs for various applications in both augmenting the clinical work flow and in academics.
Read More
Authors: Jung Daniel
Kumar Soryan, Sollee John, Choi Una, Lin Cheng Ting, Bai Harrison, Jiao Zhicheng
Final Pr. ID: Poster #: SCI-037
The purpose of this study is to develop a deep learning algorithm for detecting COVID-19 in chest x-rays of pediatric patients. Read More
Authors: Kumar Soryan , Sollee John , Choi Una , Lin Cheng Ting , Bai Harrison , Jiao Zhicheng