I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks

Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts. This makes CBMs attractive for high-stakes decision-making. In this paper, we take the quality assessment of fetal ultrasound scans as a real-life use case for CBM decision support in healthcare. For this case, simple binary concepts are not sufficiently reliable, as they are mapped directly from images of highly variable quality, for which variable model calibration might lead to unstable binarized concepts. Moreover, scalar concepts do not provide the intuitive spatial feedback requested by users. To address this, we design a hierarchical CBM imitating the sequential expert decision-making process of "seeing", "conceiving" and "concluding". Our model first passes through a layer of visual, segmentation-based concepts, and next a second layer of property concepts directly associated with the decision-making task. We note that experts can intervene on both the visual and property concepts during inference. Additionally, we increase the bottleneck capacity by considering task-relevant concept interaction. Our application of ultrasound scan quality assessment is challenging, as it relies on balancing the (often poor) image quality against an assessment of the visibility and geometric properties of standardized image content. Our validation shows that -- in contrast with previous CBM models -- our CBM models actually outperform equivalent concept-free models in terms of predictive performance. Moreover, we illustrate how interventions can further improve our performance over the state-of-the-art

What should be included in the assessment of laypersons' paediatric basic life support skills?:Results from a Delphi consensus study

Abstract Background Assessment of laypersons’ Paediatric Basic Life Support (PBLS) skills is important to ensure acquisition of effective PBLS competencies. However limited evidence exists on which PBLS skills are essential for laypersons. The same challenges exist with respect to the assessment of foreign body airway obstruction management (FBAOM) skills. We aimed to establish international consensus on how to assess laypersons’ PBLS and FBAOM skills. Methods A Delphi consensus survey was conducted. Out of a total of 84 invited experts, 28 agreed to participate. During the first Delphi round experts suggested items to assess laypersons’ PBLS and FBAOM skills. In the second round, the suggested items received comments from and were rated by 26 experts (93%) on a 5-point scale (1 = not relevant to 5 = essential). Revised items were anonymously presented in a third round for comments and 23 (82%) experts completed a re-rating. Items with a score above 3 by more than 80% of the experts in the third round were included in an assessment instrument. Results In the first round, 19 and 15 items were identified to assess PBLS and FBAOM skills, respectively. The ratings and comments from the last two rounds resulted in nine and eight essential assessment items for PBLS and FBAOM skills, respectively. The PBLS items included: “Responsiveness”,” Call for help”, “Open airway”,” Check breathing”, “Rescue breaths”, “Compressions”, “Ventilations“, “Time factor” and “Use of AED”. The FBAOM items included: “Identify different stages of foreign body airway obstruction”, “Identify consciousness”, “Call for help”, “Back blows“, “Chest thrusts/abdominal thrusts according to age”, “Identify loss of consciousness and change to CPR”, “Assessment of breathing” and “Ventilation”. Discussion For assessment of laypersons some PBLS and FBAOM skills described in guidelines are more important than others. Four out of nine of PBLS skills focus on airway and breathing skills, supporting the major importance of these skills for laypersons’ resuscitation attempts. Conclusions International consensus on how to assess laypersons’ paediatric basic life support and foreign body airway obstruction management skills was established. The assessment of these skills may help to determine when laypersons have acquired competencies. Trial registration Not relevant

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health. Such examination involves several steps that must be performed correctly: identifying suitable sites on the umbilical artery for the measurement, acquiring the blood flow curve in the form of a Doppler spectrum, and ensuring compliance to a set of quality standards. These steps rely heavily on the operator's skill, and the shortage of experienced sonographers has thus created a demand for machine assistance. In this work, we propose an automatic system to fill the gap. By using a modified Faster R-CNN network, we obtain an algorithm that can suggest locations suitable for Doppler measurement. Meanwhile, we have also developed a method for assessment of the Doppler spectrum's quality. The proposed system is validated on 657 images from a national ultrasound screening database, with results demonstrating its potential as a guidance system.Comment: Fetal Ultrasound, Umbilical Artery, Doppler Ultrasoun

Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.Comment: 10 page

DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation

Curvilinear structure segmentation plays an important role in many applications. The standard formulation of segmentation as pixel-wise classification often fails to capture these structures due to the small size and low contrast. Some works introduce prior topological information to address this problem with the cost of expensive computations and the need for extra labels. Moreover, prior work primarily focuses on avoiding false splits by encouraging the connection of small gaps. Less attention has been given to avoiding missed splits, namely the incorrect inference of structures that are not visible in the image. In this paper, we present DTU-Net, a dual-decoder and topology-aware deep neural network consisting of two sequential light-weight U-Nets, namely a texture net, and a topology net. The texture net makes a coarse prediction using image texture information. The topology net learns topological information from the coarse prediction by employing a triplet loss trained to recognize false and missed splits, and provides a topology-aware separation of the foreground and background. The separation is further utilized to correct the coarse prediction. We conducted experiments on a challenging multi-class ultrasound scan segmentation dataset and an open dataset for road extraction. Results show that our model achieves state-of-the-art results in both segmentation accuracy and continuity. Compared to existing methods, our model corrects both false positive and false negative examples more effectively with no need for prior knowledge.Comment: 9 pages, 4 figure