Negative / Null Result ReportOpen accessEngineering
Rafael Cabral, Ricardo Santos, José A.F.O. Correia et al. · 2025 · Sensors
The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evalua
View details →DOI: 10.3390/s25216568Cited by 9 Negative / Null Result ReportOpen accessMedicine
Mateja Napravnik, Franko Hržić, Martin Urschler et al. · 2025 · Scientific Reports
Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise…
View details →DOI: 10.1038/s41598-025-05009-wCited by 9 Negative / Null Result ReportOpen accessEngineering
Lihua Liang, Xiaodong Li, Shutong Liu et al. · 2026 · Buildings
This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector mode
View details →DOI: 10.3390/buildings16061118Cited by 9 Negative / Null Result ReportOpen accessComputer Science
Muhammad Dawood, Kim Branson, Sabine Tejpar et al. · 2026 · Nature Biomedical Engineering
Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependen
View details →DOI: 10.1038/s41551-026-01616-8Cited by 9 Negative / Null Result Report
Mohammad Ehsanul Karim · 2024 · BMC Medical Research Methodology
Abstract Purpose Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised…
View details →DOI: 10.1186/s12874-024-02284-5Cited by 8 Negative / Null Result Report
Taisaku Ogawa, Koji Ochiai, Tomoharu Iwata et al. · 2022 · PLOS ONE
Developments in high-throughput microscopy have made it possible to collect huge amounts of cell image data that are difficult to analyse manually. Machine learning (e.g., deep learning) is often employed to automate the extraction of…
View details →DOI: 10.1371/journal.pone.0262397Cited by 4 Negative / Null Result ReportOpen accessComputer Science
D. Abler, O. Pusterla, A. Joye-Kühnis et al. · 2025 · arXiv
Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis ( 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2)
Negative / Null Result ReportMedicine
Nomura, Kimata, Ito et al. · 2026 · Diagnostics (Basel, Switzerland)
Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the…
View details →DOI: 10.3390/diagnostics16121756 Negative / Null Result ReportMedicine
Xu, Wei, Zhang et al. · 2026 · Ultrasound in medicine & biology
To develop and validate a deep learning (DL) model based on feature fusion with B-mode ultrasound (BMUS) and contrast enhanced ultrasound (CEUS) images for non-invasive diagnosis of benign and malignant focal liver lesions (FLLs),…
View details →DOI: 10.1016/j.ultrasmedbio.2026.01.002 Negative / Null Result ReportMedicine
Tang, Xu, Zhao et al. · 2026 · Journal of clinical ultrasound : JCU
To investigate the performance of an artificial intelligence (AI) diagnostic system for thyroid nodule sonography based on deep learning convolutional neural network (CNN). We retrospectively included 485 thyroid nodules with definite…
View details →DOI: 10.1002/jcu.70275 Negative / Null Result ReportMedicine
Abdolahi, Li, Zou et al. · 2026 · Translational vision science & technology
To assess the performance of an artificial intelligence deep learning (DL) model compared with neuro-ophthalmologists for the classification of subjects with elevated intracranial pressure (ICP) and papilledema versus control subjects,…
View details →DOI: 10.1167/tvst.15.4.2 Negative / Null Result ReportMedicine
Ganjee, Bandos, Hossain et al. · 2026 · Journal of imaging informatics in medicine
Breast density is a breast cancer risk factor. The accurate quantification of breast density requires reliable segmentation of dense tissue in mammograms, but it is a challenging task due to large variations in tissue appearance across…
View details →DOI: 10.1007/s10278-026-02029-4 Negative / Null Result ReportMedicine
Shin, Kim, Lee · 2026 · European journal of radiology
To evaluate the effect of deep learning-based denoising on image quality and diagnostic performance of low-dose abdominal CT in diagnosing acute appendicitis, and to determine whether filtered back projection (FBP) or iterative…
View details →DOI: 10.1016/j.ejrad.2026.113037 Negative / Null Result ReportOpen accessMedicine (General)
Jae-Ik Lee, Dong-Hyun Kim, Hyun-Jin Yoo et al. · 2021 · Diagnostics
This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method co
View details →DOI: 10.3390/diagnostics11071225 Negative / Null Result ReportOpen accessMedicine
Mitsea A, Christoloukas N, Rontogianni A et al. · 2026 · Journal of imaging
AI methods (machine learning and deep learning methods) presented promising results concerning the accuracy of dental age estimation and sex determination. Therefore, this pilot study aims to evaluate the efficacy of an artificial…
View details →DOI: 10.3390/jimaging12060239 Negative / Null Result ReportMedicine
Schonhaut EB, Scherpereel KL, Young AJ · 2026 · IEEE transactions on bio-medical engineering
Objective Accurate, non-invasive methods for estimating joint and muscle physiological states have the potential to greatly enhance control of wearable devices during real-world ambulation. Traditional modeling approaches and current…
View details →DOI: 10.1109/tbme.2025.3577084 Negative / Null Result Report
Baek JS, Lokhande A, Neuenschwander D et al. · 2026 · Preprint
Purpose To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware AI strategy to discourage over-correction of glaucomatous defects. Design Retrospective…
View details →DOI: 10.64898/2026.05.29.26354019 Negative / Null Result ReportOpen accessMedicine
Yu J, Grude O, Berg EAR et al. · 2026 · Intensive care medicine experimental
Background Measuring mitral annular plane systolic excursion (MAPSE) serially in a single wall may be an effective method for monitoring global left ventricular (LV) function, especially when automated with a novel deep learning method…
View details →DOI: 10.1186/s40635-026-00898-1 Negative / Null Result Report
Bauer L, Laumer T · 2026 · Preprint
Abstract Fused layer modelling (FLM) is an additive manufacturing technique of growing relevance. Therefore, quality requirements regarding component properties, reproducibility and waste reduction are also rising. To achieve this, process…
View details →DOI: 10.21203/rs.3.rs-8721830/v1 Failed Experiment ReportOpen accessMedicine
López-Sagaseta J, Urdiciain A · 2025 · Scientific reports
Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino…
View details →DOI: 10.1038/s41598-025-89516-w Negative / Null Result Report
Sato T, Nishitsuka K, Itoh T et al. · 2025 · Preprint
Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providing limited interpretability at the lesion level. This study aimed to develop an…
View details →DOI: 10.1101/2025.10.01.25337115 Negative / Null Result ReportOpen accessMedicine
Biçengil K, Kurt A, Naralan ME et al. · 2026 · Diagnostics (Basel, Switzerland)
Objectives : To evaluate the diagnostic performance of a YOLOv8x-based deep learning model for detecting approximal, occlusal and buccal caries on paediatric panoramic radiographs and to compare its performance with human observers with…
View details →DOI: 10.3390/diagnostics16081150 Negative / Null Result ReportMedicine
Wu F, Wu R, Chen L et al. · 2026 · New biotechnology
Enhancing enzyme thermostability is crucial for industrial applications requiring robust performance under extreme conditions. Structure-based protein design models excel at improving thermal stability but often compromise enzymatic…
View details →DOI: 10.1016/j.nbt.2026.01.009 Negative / Null Result ReportMedicine
Brunekreef J · 2024 · Journal of imaging informatics in medicine
The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scans. The proposed algorithm attains impressive performance on the test dataset with a mean…
View details →DOI: 10.1007/s10278-024-01129-3 Negative / Null Result ReportMedicine
Kabuga E, Nandi D, Burrell S et al. · 2026 · The Journal of the Acoustical Society of America
Individual animal identification is essential for wildlife conservation and management, aiding in estimating abundance and related parameters. The feasibility of identifying individual field crickets (Plebeiogryllus guttiventris) from…
View details →DOI: 10.1121/10.0044100 Negative / Null Result ReportMedicine
Hatipoğlu Ö, Başar Ö, Mağat G et al. · 2026 · Scientific reports
Accurate detection of overhanging dental restorations on bitewing radiographs is clinically important but remains challenging due to subtle marginal discrepancies. This study aimed to develop and compare deep learning-based instance…
View details →DOI: 10.1038/s41598-026-57540-z Negative / Null Result ReportMedicine
Li RQ, Su S, Zhan LP et al. · 2026 · Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
Objective: To investigate the differences in the changes of periodontal ligament area (PDLA) and related clinical indicators before and after maxillary central incisor movement under different orthodontic treatment regimens. Methods: This…
View details →DOI: 10.3760/cma.j.cn112144-20250722-00280 Negative / Null Result ReportMedicine
Jiang D, Zhang W, Liu L et al. · 2026 · BMC cardiovascular disorders
Background Accurate quantification of aortic valve calcification (AVC) on contrast-enhanced computed tomography angiography (CTA) is pivotal for planning surgical and transcatheter aortic valve replacement. The optimal Hounsfield unit (HU)…
View details →DOI: 10.1186/s12872-026-06108-w Negative / Null Result ReportMedicine
Folcher A, Piters J, Wallach D et al. · 2025 · Journal of neuroradiology = Journal de neuroradiologie
Background The Thrombolysis in Cerebral Infarction (TICI) scale is widely used to assess angiographic outcomes of mechanical thrombectomy despite significant variability. Our objective was to create and optimize an artificial intelligence…
View details →DOI: 10.1016/j.neurad.2025.101366 Negative / Null Result Report
Riepe TV, de Bruijn SE, Roosing S et al. · 2025 · Preprint
Splice prediction tools can be used to identify splice-altering variants in patients with inherited diseases. Since splicing is tissue-specific, a predicted splice defect may vary depending on the tissue of interest. Current splice…
View details →DOI: 10.1101/2025.02.10.637427 