Abstract
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middleincome countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.83950.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions.

Abstract
Abstract Background. Breast cancer treatment has improved overall survival rates, with different locoregional approaches offering patients similar locoregional control but variable aesthetic outcomes that may lead to disappointment and poor quality of life (QoL). There are no standardized methods for informing patients of the different therapies prior to intervention, nor validated tools for evaluation of aesthetics and patients' expectations. The CINDERELLA Project is based on years of research and developments of new healthcare technologies by various partners, aimed to provide an artificial intelligence (AI) tool to aid shared decision-making by showing breast cancer patients the predicted aesthetic outcomes of their locoregional treatment. The clinical trial will evaluate the use of this tool within an AI cloud-based platform approach (CINDERELLA App) versus a standard approach. We anticipate that the CINDERELLA App will lead to improved satisfaction, psychosocial well-being and health-related QoL while maintaining the quality of care and providing environmental and economic benefits. Trial design. CINDERELLA is an international multicentric interventional randomized controlled open-label clinical trial. Using the CINDERELLA App, the AI and Digital Health arm will provide patients with complete information about the proposed types of locoregional treatments and photographs of similar patients previously treated with the same techniques. The Control arm will follow the standard approach of each clinical site. Randomization will be conducted online using the digital health platform CANKADO, ensuring a balanced distribution of participants between the two groups. CANKADO is the underlying platform through which physicians control the patients' app content and conduct all data collection. Privacy, data protection and ethical principles in AI usage were taken into account. Eligibility criteria. Patients diagnosed with primary breast cancer without evidence of systemic disease. All patients must sign an informed consent and be able to use a web-based app autonomously or with home-based support. Specific aims. Primary objective: to assess the levels of agreement among patients' expectations regarding the aesthetic outcome before and 12 months after locoregional treatment. The trial will also evaluate the aesthetic outcome level of agreement between the AI evaluation tool and self-evaluation. Secondary objectives: health-related QoL (EQ-5D-5L and BREAST-Q ICHOM questionnaires) and resource consumption (e.g., time spent in the hospital, out-of-pocket expenses). The questionnaires and photographs will be applied prior to any treatment, at wound healing, at 6 and 12 months following the completion of locoregional therapy. Statistical methods. Wilcoxon signed rank test will be used to assess the intervention's impact on the agreement level between expectations and obtained results. Weighted Cohen's kappa will be calculated to measure the improvement in classifying aesthetic results with intervention. Statistical tests and/or bootstrap techniques will compare results between arms. A similarity measure will be calculated between self-evaluation and outcome obtained with the AI tool for each participant, and a beta regression model will be used to analyze the intervention's effect. Secondary objectives will be evaluated by scoring questionnaires based on provided guidelines. Target accrual. The clinical trial, led by Champalimaud Clinical Centre, will enroll a minimum of 515 patients in each arm between July 2023 and January 2025. Recruitment is currently open at five study sites in Germany, Israel, Italy, Poland and Portugal. The clinical trial is still open for further international study sites. Funding. European Union grant HORIZON-HLTH-2021-DISEASE-04-04 Agreement No. 101057389. Citation Format: Eduard-Alexandru Bonci, Orit Kaidar-Person, Marília Antunes, Oriana Ciani, Helena Cruz, Rosa Di Micco, Oreste Davide Gentilini, Nicole Rotmensz, Pedro Gouveia, Jörg Heil, Pawel Kabata, Nuno Freitas, Tiago Gonçalves, Miguel Romariz, Helena Montenegro, Hélder P. Oliveira, Jaime S. Cardoso, Henrique Martins, Daniela Lopes, Marta Martinho, Ludovica Borsoi, Elisabetta Listorti, Carlos Mavioso, Martin Mika, André Pfob, Timo Schinköthe, Giovani Silva, Maria-Joao Cardoso. CINDERELLA Clinical Trial (NCT05196269): using artificial intelligence-driven healthcare to enhance breast cancer locoregional treatment decisions [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-11.

Abstract
The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBDCAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.

Abstract
As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our results highlighted increased performance and reduced bias for all our experiments. In addition, we further show that having biased/specialized teachers is crucial by showing that our approach achieves better results than when knowledge is distilled from four teachers trained on balanced datasets. Our approach represents a step forward to the understanding of the importance of ethnicity-specific features.

Abstract
Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors; for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a perfect world, without pulse oximetry bias, using SaO(2) (blood-gas), to the actual world, with biased measurements, using SpO(2) (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO(2) are a control and models using SpO(2) a treatment. The blood-gas oximetry linked dataset was a suitable testbed, containing 163,396 nearly-simultaneous SpO(2) - SaO(2) paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 h, and SOFA score increase by two points. Models using SaO(2) instead of SpO(2) generally showed better performance. Patients with overestimation of O-2 by pulse oximetry of >= 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P < 0.001. This mirrors clinical processes where biased pulse oximetry readings provide clinicians with false reassurance of patients' oxygen levels. A similar degradation happened in ML models, with pulse oximetry biases leading to more false negatives in predicting adverse outcomes.

Abstract