A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography.

The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images.

Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results.

[Simple cysts of the liver]. / Le cisti semplici del fegato.

OBJECTIVE: To evaluate the treatment of symptomatic benign non-parasitic cysts of the liver by percutaneous drainage and sclerotherapy with alcohol or surgery. DESIGN: Descriptive, prospective. SETTING: The study was conducted at the University Hospital of Catania (Italy), which serves as a general community hospital. SUMMARY BACKGROUND DATA: Solitary biliary cysts are among the most frequent cystic lesions of the liver and have a prevalence of 1 to 2 percent. They are almost always asymptomatic and do not require treatment. Ultrasonography shows a regular, round or oval, entirely liquid and trans-sonic image sufficient to make the diagnosis. Complications are exceptional. PATIENTS: Eight out of 40 patients who presented with symptomatic benign non-parasitic cysts of the liver during the period 1987-1994 and in whom percutaneous drainage was not contraindicated. INTERVENT: Drainage sclerotherapy with absolute alcohol was carried out, after which suction was applied until oozing stopped. RESULTS: Eight patients were treated, all women, 49-61 years old. In 7 patients, the cyst did not recur during the follow-up period which ranged from 8 to 60 months. Only one patient needed another percutaneous drainage. No complications of the drainage were encountered. CONCLUSION: Percutaneous drainage followed by alcohol sclerotherapy and suction is the treatment of choice in patients with symptomatic benign non-parasitic cysts of the liver. Surgical treatment should be reserved for patients who fail to respond to repeated percutaneous drainage and cases in which the location of the cyst makes it technically difficult to use a percutaneous route.