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Background/Objectives: Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is an important prognostic marker in HER2-positive breast cancer (BC). However, reliable pre-treatment predictors based on routinely available clinical data remain limited. This study evaluated whether clinicopathologic and baseline magnetic resonance imaging (MRI) variables could predict NAC response using classical machine learning (ML) approaches. Methods: A retrospective cohort of 112 patients with HER2-positive BC treated with NAC was analysed, including 57 patients who achieved pCR and 55 who did not. Fourteen pre-treatment variables were evaluated, including hormone receptor (HR) status, tumour grade, ER and PR expression, Ki67, nodal status, age, and baseline MRI characteristics. Seventy ML classifiers were compared using a fully nested leave-one-out cross-validation (LOOCV) framework. Performance was assessed using accuracy, area under the receiver operating characteristic curve (AUROC), Cohen’s kappa, sensitivity, specificity, and F1-score. Results: A support vector machine (SVM) with a radial basis function (RBF) kernel achieved the highest observed accuracy (82.1%) among the 70 evaluated classifiers. The corresponding AUROC was 83.3%, Cohen’s kappa was 64.2%, and sensitivity, specificity, and F1-score were 87.7%, 76.4%, and 83.3%, respectively. HR-related variables, particularly PR and ER expression, together with Ki67 and baseline MRI features, ranked among the most influential predictors. Despite relying exclusively on routinely available pre-treatment variables, the model demonstrated meaningful predictive performance. Conclusions: ML applied to routinely available clinicopathologic and baseline MRI variables showed promising ability to predict pCR before treatment initiation in HER2-positive BC. The proposed approach may support pre-treatment clinical risk assessment using information already generated during routine clinical assessment. Nevertheless, prospective multicentre external validation, calibration assessment, and evaluation of clinical utility are required before implementation in routine clinical practice.

Background/Objective: Preoperative prediction of third molar eruption with artificial intelligence (AI) has been a challenge in dentistry and oral surgery. Methods: In this investigation, we used machine learning (ML) algorithms to characterize M3 panoramic radiological images for the preoperative differential diagnosis of third nolar eruption/retention and compared them with clibical explorations to validate their performance. Results: We retrospectively collected data of patients with mandibular thid molar retention, where all eruption diagnoses were confirmed via clinical exploration. A total of 383 panoramic radiographies were selected to train the PDApp software for eruption diagnosis for the software validation. Conclusions: The PDApp software achieved the highest performance metrics for the prediction of mandibular third molar eruption

Background: Preoperative prediction of mandibular third molar (M3) eruption remains a major challenge in dentistry and oral surgery. Artificial intelligence (AI) offers new opportunities to improve diagnostic accuracy and reduce the subjectivity associated with manual assessment. Objective: This study aimed to develop and validate PDApp, a machine-learning-based software designed to predict third lower molar eruption status (erupted vs. retained) using panoramic radiographic images, and to compare its diagnostic performance with clinical exploration. Materials and Methods: A retrospective dataset of 383 mandibular third molars with clinically confirmed eruption status was collected. Panoramic radiographic images were processed and used to train multiple machine learning (ML) algorithms integrated into PDApp for eruption prediction. The software’s performance metrics were analyzed and validated against clinical exploration, considered the diagnostic reference standard. Results: PDApp achieved the highest performance metrics among the evaluated ML approaches, reaching an accuracy of up to 99.5% in predicting mandibular third molar eruption. The tool showed strong reliability in differentiating erupted from retained M3 using panoramic radiographs. Conclusions: PDApp represents a robust, accurate, and easy-to-use AI-based tool for predicting third molar eruption potential in adolescent and teenage patients. Its implementation may enhance diagnostic efficiency, reduce common errors associated with manual evaluation, and support clinical decision-making in the management of impacted mandibular third molars. Future work will focus on integrating automatic calculation of radiographic ratios to achieve a fully automated prediction workflow.

Numerous studies, diagnoses, and projects have been carried out in recent years to analyze the low female presence in STEM studies. However, progress has been limited, and the female presence is still low in certain degrees related to information and communication technologies, physics, and engineering. Many of the actions have been aimed at attracting women to these fields, but few have tried to change the culture of these disciplines, which make them a non-place for women. This paper analyses the measures carried out in Spanish public universities, and specifically at the University of Santiago de Compostela, to contribute to making these disciplines a place for women. Computer engineering workshops for primary and secondary education are proposed, incorporating a gender perspective. These transformative activities were highly valuated and welcomed by non-university teachers. The ideas inspiring these initiatives might help both to attract girls to STEM degrees and to generate gender equality environments, in order to change the androcentric culture of this field.

Las mujeres están infrarrepresentadas en las disciplinas técnico-científicas debido a los estereotipos de género. Este artículo describe los talleres realizados para introducir conceptos relacionados con la ingeniería, la informá-tica y el pensamiento computacional con perspectiva de género desde la educación infantil, con el fin de promover vocaciones femeninas y un desarrollo en igualdad desde la infancia. Se trata de actividades próximas a las vivencias de la infancia, que huyen de los estereotipos de género que destila el uso de la tecnología. Estas actividades usan experiencias vitales del juego del alumnado y se integran con el resto de contenidos en matemáticas, lengua o ciencias naturales. Algunas actividades son la simulación de un robot cocinero con nuestro cuerpo o la construcción de un programa como un cuento infantil. La acogida del alumnado y profesorado en los colegios ha sido muy positiva.