PRO-READ IR: Enhanced PROcedural Information READability for Patient-Centered Care in Interventional Radiology With Large Language Models.

PURPOSE: To evaluate the extent to which Generative Pre-trained Transformer 4 (GPT-4) can educate patients by generating easily understandable information about the most common interventional radiology (IR) procedures. MATERIALS AND METHODS: We reviewed 10 IR procedures and prepared prompts for GPT-4 to provide patient educational instructions about each procedure in layman's terms. The instructions were then evaluated by four clinical physicians and nine nonclinical assessors to determine their clinical appropriateness, understandability, and clarity using a survey. A grade-level readability assessment was performed using validated metrics to evaluate accessibility to a wide patient population. The same procedures were also evaluated from the patient instructions available at radiologyinfo.org and compared with GPT-generated instructions utilizing a paired t test. RESULTS: Evaluation by four clinical physicians shows that nine GPT-generated instructions were fully appropriate, whereas arterial embolization instructions was somewhat appropriate. Evaluation by nine nonclinical assessors shows that paracentesis, dialysis catheter placement, thrombectomy, ultrasound-guided biopsy, and nephrostomy-tube instructions were rated excellent by 57% and good by 43%. The arterial embolization and biliary-drain instructions were rated excellent by 28.6% and good by 71.4%. In contrast, thoracentesis, port placement, and CT-guided biopsy instructions received 43% excellent, 43% good, and 14% fair. The readability assessment across all procedural instructions showed a better Flesch-Kincaid mean grade of GPT-4 instructions compared with radiologyinfo.org (7.8 ± 0.87 versus 9.6 ± 0.83; P = .007) indicating excellent readability at 7th- to 8th-grade level compared with 9th to 10th grade. Additionally there was a lower Gunning Fog mean index (10.4 ± 1.2 versus 12.7 ± 0.93; P = .006), and higher Flesch Reading Ease mean score (69.4 ± 4.8 versus 51.3±3.9; P = .0001) indicating better readability. CONCLUSION: IR procedural instructions generated by GPT-4 can aid in improving health literacy and patient-centered care in IR by generating easily understandable explanations.

Transient, Image-Guided Gel-Dissection for Percutaneous Thermal Ablation.

Image-guided tumor ablative therapies are mainstay cancer treatment options but often require intra-procedural protective tissue displacement to reduce the risk of collateral damage to neighboring organs. Standard of care strategies, such as hydrodissection (fluidic injection), are limited by rapid diffusion of fluid and poor retention time, risking injury to adjacent organs, increasing cancer recurrence rates from incomplete tumor ablations, and limiting patient qualification. Herein, a "gel-dissection" technique is developed, leveraging injectable hydrogels for longer-lasting, shapeable, and transient tissue separation to empower clinicans with improved ablation operation windows and greater control. A rheological model is designed to understand and tune gel-dissection parameters. In swine models, gel-dissection achieves 24 times longer-lasting tissue separation dynamics compared to saline, with 40% less injected volume. Gel-dissection achieves anti-dependent dissection between free-floating organs in the peritoneal cavity and clinically significant thermal protection, with the potential to expand minimally invasive therapeutic techniques, especially across locoregional therapies including radiation, cryoablation, endoscopy, and surgery.

Catheter Directed Thrombectomy and Other Deep Venous Interventions in Cancer Patients.

Treating cancer patients with deep venous thrombosis/venous thromboembolism (DVT/VTE) can be challenging as patients are frequently unable to receive the standard therapy of anticoagulation due to the increased risk of bleeding complications seen in this population. Similarly, the hesitation of interventionalists to use thrombolytic agents due to bleeding risks limits percutaneous intervention options as well. Further, outcome data and guidelines do not exist for oncologic patients and often treatment is tailored to patient-specific factors after multidisciplinary discussion. This article reviews specific factors to consider when planning percutaneous treatment of cancer patients with DVT/VTE, focusing on the iliocaval system.

Preventing inadvertent drain removal using a novel catheter securement device.

Percutaneous drains have provided a minimally invasive way to treat a wide range of disorders from abscess drainage to enteral feeding solutions to treating hydronephrosis. These drains suffer from a high rate of dislodgement of up to 30% resulting in emergency room visits, repeat hospitalizations, and catheter repositioning/replacement procedures, which incur significant morbidity and mortality. Using ex vivo and in vivo models, a force body diagram was utilized to determine the forces experienced by a drainage catheter during dislodgement events, and the individual components which contribute to drainage catheter securement were empirically collected. Prototypes of a skin level catheter securement and valved quick release system were then developed. The system was inspired by capstans used in boating for increasing friction of a line around a central spool and quick release mechanisms used in electronics such as the Apple MagSafe computer charger. The device was tested in a porcine suprapubic model, which demonstrated the effectiveness of the device to prevent drain dislodgement. The prototype demonstrated that the miniaturized versions of technologies used in boating and electronics industries were able to meet the needs of preventing dislodgement of patient drainage catheters.

Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

PURPOSE OF REVIEW: The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology. RECENT FINDINGS: With advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged. In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.

Delayed and Chronic Sequelae of Trauma and the Role of the Interventional Radiologist.

In addition to acute injury requiring interventional radiologic treatments, patients with traumatic injuries can develop delayed or chronic complications. These injuries can involve nearly all solid organs in the abdomen. Coupled with significant improvements in visualizing these injuries with advanced imaging techniques such as minimally invasive procedures, nonoperative management of both acute traumatic injuries and their longer term sequelae has become the norm. This article reviews frequently seen complications of traumatic injury and their management by interventional radiologists.

Infection Control in Interventional Radiology During the COVID-19 Era.

The COVID-19 pandemic has challenged the capacity of interventional radiology departments worldwide to effectively treat COVID-19 and non-COVID-19 patients while preventing disease transmission among patients and healthcare workers. In this review, we describe the various data driven infection control measures implemented by the interventional radiology department of a large tertiary care center in the United States including the use and novel re-use of personal protective equipment, COVID-19 testing strategies, modifications in procedural workflows and the leveraging of telehealth visits. Herein, we provide effective triage, procedural, and management algorithms that may guide other interventional radiology departments during the ongoing COVID-19 pandemic and in future infectious disease outbreaks.