Latest research
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Präoperative Planung in der mikroinvasiven Herzchirurgie
In der mikroinvasiven („µ-invasiven“) Herzchirurgie, die durch den Verzicht auf Rippenspreizer, vollendoskopische Sicht und perkutane Kanülierung definiert ist wird die präoperative Planung zum entscheidenden Erfolgsfaktor. Die minimal- und...
Multi-region ultrasound radiomics combining whole-tumor, habitat subregionand peritumoral features for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma
This study aimed to develop a ultrasound radiomics model for preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) and to evaluate the predictive value of a clinic-radiomics combined model. A total of 390...
Scoping review of methodology for aiding generalisability and transportability of clinical prediction models
Background Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a...
Diagnostic performance of multiparametric model integrating ¹⁸F-MFBG PET/CT imaging parameters and clinical factors for differentiating neuroblastoma from ganglioneuroblastoma: a preliminary study
Purpose To develop and preliminarily validate a multiparametric diagnostic model integrating ¹⁸F-meta-fluorobenzylguanidine (¹⁸F-MFBG) PET/CT imaging parameters with clinical factors for noninvasive differentiation of neuroblastoma (NB) from...
Machine learning prediction of mitral regurgitation improvement after TAVI
The fate of mitral regurgitation after transcatheter aortic valve implantation is highly heterogeneous, and accurate preoperative prediction of mitral regurgitation improvement remains challenging. This study aimed to develop and validate a machine...
Development and validation of a nomogram for preoperative prediction of central lymph node metastasis in patients With cT1-2N0M0 papillary thyroid carcinoma
Background To develop and validate a nomogram based on LASSO and multivariable logistic regression for predicting preoperative central lymph node metastasis (CLNM) in patients with cT1–2N0M0 papillary thyroid carcinoma (PTC), and to assess its...
Prediction of mammaprint risk in breast cancer using a combined radiomics-habitat model integrating intratumoral and peritumoral MRI features: a retrospective dual-center study
Background To develop and validate an MRI-based radiomics-habitat model integrating intratumoral and peritumoral features to noninvasively predict MammaPrint risk. Methods This retrospective dual-center study included 156 patients who underwent...
Reframing epidemic early warning: toward AI-native decision intelligence for public health governance
Epidemic early warning has been framed mainly as prediction, yet sophisticated forecasts often fail to translate into timely, coordinated action. We propose reframing early warning as the perceptual foundation of AI-native decision intelligence for...
Contrastive learning-guided multiple instance learning for automated diagnosis of chronic endometritis from digitized histopathology
Background Chronic endometritis (CE) is a common yet underdiagnosed endometrial inflammatory disorder. This study aimed to develop and validate a deep learning model for direct CE detection from H&E-stained slides. Methods A retrospective dataset of...
Global trends and hotspots of the sepsis machine learning prediction model: a bibliometric analysis
Background Sepsis is a systemic syndrome caused by infection-induced organ dysfunction, and machine learning prediction models have shown important value in the early warning of sepsis. However, the overall research trends, main research topics, and...
Development and external validation of an explainable machine-learning model for early complications following robot-assisted radical prostatectomy: a multicenter study
Complications occurring early after robot-assisted radical prostatectomy (RARP) can hinder postoperative recovery. Existing risk instruments, however, generally incorporate few predictors and assume linear associations. We aimed to derive and...
Machine learning-based predictive model for pulmonary hypertension risk stratification in chronic kidney disease: development, internal validation and temporal external validation
Background Among chronic kidney disease (CKD) patients referred for right heart catheterization (RHC) due to clinical suspicion of pulmonary hypertension (PH), PH is a highly prevalent complication and contributes to increased cardiac mortality. This...
LMRNet: a biopsy-derived deep learning model for predicting lymph node metastasis in early gastric cancer with pathological interpretability
Introduction Early gastric cancer (EGC) typically carries a favorable prognosis, and many patients can achieve curative outcomes through local resection when lymph node metastasis (LNM) is absent. Therefore, reliable preoperative evaluation of...
Logistic principal component analysis for combining binary predictors: a comparative study based on area under the receiver operating characteristic curve performance
Background Combining binary predictors into a single diagnostic score is a common challenge in clinical research. Traditional methods such as logistic regression and unweighted sum scores (USS) are widely used but can be unstable or fail to account...
Imbalance-robust predictive maintenance using deep learning with virtual measurement modeling and explainable AI
Predictive maintenance (PdM) has become a key enabler of intelligent industrial systems; however, its effectiveness is often constrained by measurement uncertainty, the absence of raw sensor signals, and severe class imbalance between normal and...
Foundation models for tabular medical detection: a robust and generalizable framework for clinical decision support
In clinical prediction, tabular data needs to be processed, which is difficult due to small, heterogeneous and imbalanced medical data sets. This work compares the performance of TabPFN V2, a tabular foundation model, across four public medical...
Scoping review of models predicting emergency department length of stay
Background Emergency Department (ED) length of stay (LOS) is a critical performance indicator in healthcare systems, influencing patient outcomes, overcrowding, and resource utilization. Predicting LOS can enhance patient flow and resource...
Therapierelevante genetische und somatische Biomarker in der gynäkologischen Onkologie
Die gynäkologische Onkologie ist durch eine zunehmende biologische Differenzierung maligner Erkrankungen geprägt. Therapierelevante Biomarker spielen eine zentrale Rolle bei der Auswahl zielgerichteter, immunonkologischer und zunehmend...
Prediction markets as a collective expectation signal for disease surveillance
Public health increasingly uses unofficial signals such as rumors, media, search, and self-reported symptoms to detect emergencies before official counts. Commercial prediction markets, where people bet on disease events, are a candidate new source,...
