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1.
Int J Colorectal Dis ; 39(1): 21, 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38273097

RESUMO

PURPOSE: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. METHODS: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). RESULTS: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). CONCLUSION: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.


Assuntos
Laparoscopia , Algoritmo Florestas Aleatórias , Humanos , Estudos de Coortes , Laparoscopia/métodos , Estudos Retrospectivos , Resultado do Tratamento
2.
Eur Radiol ; 33(3): 1844-1851, 2023 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-36282311

RESUMO

OBJECTIVE: To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures. METHODS: In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation. RESULTS: Diagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI 1.5 to 4.0 %, p = 0.045) and trust in the algorithm was generated primarily by the certainty of the prediction (100% of participants). Different human-AI interactions were observed ranging from nearly no interaction to humanization of the algorithm. High scores in neuroticism were correlated with higher persuasibility (Pearson's r = 0.98, p = 0.02), while higher consciousness and change of accuracy showed an inverse correlation (Pearson's r = -0.96, p = 0.04). CONCLUSION: Trust in the algorithm's performance was mostly dependent on the certainty of the predictions in combination with a plausible heatmap. Human-AI interaction varied widely and was influenced by personality traits. KEY POINTS: • AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions. • Trust in the algorithm's performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap. • Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm's prediction than others.


Assuntos
Neoplasias da Mama , Doenças Vasculares , Humanos , Feminino , Estudos Retrospectivos , Algoritmos , Mamografia , Radiologistas , Neoplasias da Mama/diagnóstico por imagem
3.
Eur Radiol ; 33(10): 6892-6901, 2023 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-37133518

RESUMO

OBJECTIVES: To examine the effect of high-b-value computed diffusion-weighted imaging (cDWI) on solid lesion detection and classification in pancreatic intraductal papillary mucinous neoplasm (IPMN), using endoscopic ultrasound (EUS) and histopathology as a standard of reference. METHODS: Eighty-two patients with known or suspected IPMN were retrospectively enrolled. Computed high-b-value images at b = 1000 s/mm2 were calculated from standard (b = 0, 50, 300, and 600 s/mm2) DWI images for conventional full field-of-view (fFOV, 3 × 3 × 4 mm3 voxel size) DWI. A subset of 39 patients received additional high-resolution reduced-field-of-view (rFOV, 2.5 × 2.5 × 3 mm3 voxel size) DWI. In this cohort, rFOV cDWI was compared against fFOV cDWI additionally. Two experienced radiologists evaluated (Likert scale 1-4) image quality (overall image quality, lesion detection and delineation, fluid suppression within the lesion). In addition, quantitative image parameters (apparent signal-to-noise ratio (aSNR), apparent contrast-to-noise ratio (aCNR), contrast ratio (CR)) were assessed. Diagnostic confidence regarding the presence/absence of diffusion-restricted solid nodules was assessed in an additional reader study. RESULTS: High-b-value cDWI at b = 1000 s/mm2 outperformed acquired DWI at b = 600 s/mm2 regarding lesion detection, fluid suppression, aCNR, CR, and lesion classification (p = < .001-.002). Comparing cDWI from fFOV and rFOV revealed higher image quality in high-resolution rFOV-DWI compared to conventional fFOV-DWI (p ≤ .001-.018). High-b-value cDWI images were rated non-inferior to directly acquired high-b-value DWI images (p = .095-.655). CONCLUSIONS: High-b-value cDWI may improve the detection and classification of solid lesions in IPMN. Combining high-resolution imaging and high-b-value cDWI may further increase diagnostic precision. CLINICAL RELEVANCE STATEMENT: This study shows the potential of computed high-resolution high-sensitivity diffusion-weighted magnetic resonance imaging for solid lesion detection in pancreatic intraductal papillary mucinous neoplasia (IPMN). The technique may enable early cancer detection in patients under surveillance. KEY POINTS: • Computed high-b-value diffusion-weighted imaging (cDWI) may improve the detection and classification of intraductal papillary mucinous neoplasms (IPMN) of the pancreas. • cDWI calculated from high-resolution imaging increases diagnostic precision compared to cDWI calculated from conventional-resolution imaging. • cDWI has the potential to strengthen the role of MRI for screening and surveillance of IPMN, particularly in view of the rising incidence of IPMNs combined with now more conservative therapeutic approaches.


Assuntos
Neoplasias Intraductais Pancreáticas , Neoplasias Pancreáticas , Humanos , Estudos Retrospectivos , Neoplasias Pancreáticas/diagnóstico por imagem , Razão Sinal-Ruído , Imagem de Difusão por Ressonância Magnética/métodos , Pâncreas
4.
Crit Care ; 27(1): 201, 2023 05 26.
Artigo em Inglês | MEDLINE | ID: mdl-37237287

RESUMO

BACKGROUND: A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung water index (EVLWI) extracted from the transpulmonary thermodilution (TPTD). Severity of edema from chest X-rays, to date is based on the subjective classification of radiologists. In this work, we use machine learning to quantitatively predict the severity of pulmonary edema from chest radiography. METHODS: We retrospectively included 471 X-rays from 431 patients who underwent chest radiography and TPTD measurement within 24 h at our intensive care unit. The EVLWI extracted from the TPTD was used as a quantitative measure for pulmonary edema. We used a deep learning approach and binned the data into two, three, four and five classes increasing the resolution of the EVLWI prediction from the X-rays. RESULTS: The accuracy, area under the receiver operating characteristic curve (AUROC) and Mathews correlation coefficient (MCC) in the binary classification models (EVLWI < 15, ≥ 15) were 0.93 (accuracy), 0.98 (AUROC) and 0.86(MCC). In the three multiclass models, the accuracy ranged between 0.90 and 0.95, the AUROC between 0.97 and 0.99 and the MCC between 0.86 and 0.92. CONCLUSION: Deep learning can quantify pulmonary edema as measured by EVLWI with high accuracy.


Assuntos
Aprendizado Profundo , Edema Pulmonar , Humanos , Edema Pulmonar/diagnóstico por imagem , Edema Pulmonar/etiologia , Raios X , Estudos Retrospectivos , Água Extravascular Pulmonar/diagnóstico por imagem , Radiografia , Termodiluição
5.
Eur J Nucl Med Mol Imaging ; 50(1): 115-129, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36074156

RESUMO

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis. METHODS AND MATERIALS: Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR1 area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery. RESULTS: Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration. CONCLUSION: In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.


Assuntos
Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Camundongos , Animais , Cisplatino/uso terapêutico , Ensaios Antitumorais Modelo de Xenoenxerto , Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/tratamento farmacológico , Carcinoma Ductal Pancreático/patologia , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/patologia , Neovascularização Patológica/diagnóstico por imagem , Neovascularização Patológica/tratamento farmacológico , Biomarcadores , Tomografia Computadorizada por Raios X , Imageamento por Ressonância Magnética , Tomografia , Linhagem Celular Tumoral , Gencitabina , Neoplasias Pancreáticas
6.
Entropy (Basel) ; 24(5)2022 May 16.
Artigo em Inglês | MEDLINE | ID: mdl-35626597

RESUMO

The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.

7.
Radiologe ; 60(1): 24-31, 2020 Jan.
Artigo em Alemão | MEDLINE | ID: mdl-31811324

RESUMO

BACKGROUND: The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of the individual patient. It is therefore important to deal with this topic and to develop a basic understanding of it. OBJECTIVES: This article gives an overview of the exciting and dynamic field of machine learning and serves as an introduction to some methods primarily from the realm of supervised learning. In addition to definitions and simple examples, limitations are discussed. CONCLUSIONS: The basic principles behind the methods are simple. Nevertheless, due to their high dimensional nature, the factors influencing the results are often difficult or impossible to understand by humans. In order to build confidence in the new technologies and to guarantee their safe application, we need explainable algorithms and prospective effectiveness studies.


Assuntos
Aprendizado de Máquina , Inteligência Artificial , Previsões , Humanos
8.
Radiologe ; 60(1): 32-41, 2020 Jan.
Artigo em Alemão | MEDLINE | ID: mdl-31820014

RESUMO

CLINICAL ISSUE: The reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology. METHODOLOGICAL INNOVATIONS: Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations. MATERIALS AND METHODS: This article is based on a selective literature search with the PubMed search engine. ASSESSMENT: Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.


Assuntos
Radiologia , Previsões , Humanos , Aprendizado de Máquina
9.
Pathologe ; 41(6): 649-658, 2020 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-33052431

RESUMO

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.


Assuntos
Inteligência Artificial , Diagnóstico por Imagem , Aprendizado de Máquina , Neoplasias/diagnóstico por imagem , Algoritmos , Humanos , Redes Neurais de Computação
10.
J Clin Monit Comput ; 33(1): 5-12, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-29680878

RESUMO

Extravascular lung water (index) (EVLW(I)) can be estimated using transpulmonary thermodilution (TPTD). Computed tomography (CT) with quantitative analysis of lung tissue density has been proposed to quantify pulmonary edema. We compared variables of pulmonary fluid status assessed using quantitative CT and TPTD in critically ill patients. In 21 intensive care unit patients, we performed TPTD measurements directly before and after chest CT. Based on the density data of segmented CT images we calculated the tissue volume (TV), tissue volume index (TVI), and the mean weighted index of voxel aqueous density (VMWaq). CT-derived TV, TVI, and VMWaq did not predict TPTD-derived EVLWI values ≥ 14 mL/kg. There was a significant moderate positive correlation between VMWaq and mean EVLWI (EVLWI before and after CT) (r = 0.45, p = 0.042) and EVLWI after CT (r = 0.49, p = 0.025) but not EVLWI before CT (r = 0.38, p = 0.086). There was no significant correlation between TV and EVLW before CT, EVLW after CT, or mean EVLW. There was no significant correlation between TVI and EVLWI before CT, EVLWI after CT, or mean EVLWI. CT-derived variables did not predict elevated TPTD-derived EVLWI values. In unselected critically ill patients, variables of pulmonary fluid status assessed using quantitative CT cannot be used to predict EVLWI.


Assuntos
Estado Terminal , Água Extravascular Pulmonar/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Edema Pulmonar/diagnóstico por imagem , Termodiluição/métodos , Idoso , Cuidados Críticos , Feminino , Hemodinâmica , Humanos , Unidades de Terapia Intensiva , Pulmão/irrigação sanguínea , Masculino , Pessoa de Meia-Idade , Tomografia Computadorizada por Raios X
11.
Eur Radiol ; 28(12): 4925-4931, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29808428

RESUMO

PURPOSE: The aim of this study was to evaluate the advantages of dual-layer spectral CT (DLSCT) in detection and staging of head and neck cancer (HNC) as well as the imaging of tumour margins and infiltration depth compared to conventional contrast enhanced CT (CECT). MATERIALS AND METHODS: Thirty-nine patients with a proven diagnosis of HNC were examined with a DLSCT scanner and retrospectively analysed. An age-matched healthy control group of the same size was used. Images were acquired in the venous phase. Virtual monoenergetic 40keV-equivalent (MonoE40) images were compared to CECT-images. Diagnostic confidence for tumour identification and margin detection was rated independently by four experienced observers. The steepness of the Hounsfield unit (HU)-increase at the tumour margin was analysed. External carotid artery branch image reconstructions were performed and their contrast compared to conventional arterial phase imaging. Means were compared using a Student's t-test. ANOVA was used for multiple comparisons. RESULTS: MonoE40 images were superior to CECT-images in tumour detection and margin delineation. MonoE40 showed significantly higher attenuation differences between tumour and healthy tissue compared to CECT-images (p < 0.001). The HU-increase at the boundary of the tumour was significantly steeper in MonoE40 images compared to CECT-images (p < 0.001). Iodine uptake in the tumour was significantly higher compared to healthy tissue (p < 0.001). MonoE40 compared to conventional images allowed visualisation of external carotid artery branches from the venous phase in a higher number of cases (87% vs. 67%). CONCLUSION: DLSCT enables improved detection of primary and recurrent head and neck cancer and quantification of tumour iodine uptake. Improved contrast of MonoE40 compared to conventional reconstructions enables higher diagnostic confidence concerning tumour margin detection and vessel identification. KEY POINTS: • Sensitivity concerning tumour detection are higher using dual-layer spectral-CT than conventional CT. • Lesion to background contrast in DLSCT is significantly higher than in CECT. • DLSCT provides sufficient contrast for evaluation of external carotid artery branches.


Assuntos
Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Análise de Variância , Artérias Carótidas/diagnóstico por imagem , Estudos de Casos e Controles , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Masculino , Estadiamento de Neoplasias/métodos , Estudos Retrospectivos , Sensibilidade e Especificidade
12.
IEEE Trans Med Imaging ; 43(1): 241-252, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-37506004

RESUMO

Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD_study/.


Assuntos
Algoritmos , Neuroimagem
13.
EBioMedicine ; 101: 105002, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38335791

RESUMO

BACKGROUND: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored. METHODS: In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced subgroup-AUROC (sAUROC), which aids in quantifying fairness in machine learning. FINDINGS: Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups. INTERPRETATION: Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical "fairness laws" discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition. FUNDING: European Research Council Deep4MI.


Assuntos
Algoritmos , Hidrolases , Humanos , Aprendizado de Máquina
14.
Commun Med (Lond) ; 4(1): 46, 2024 Mar 14.
Artigo em Inglês | MEDLINE | ID: mdl-38486100

RESUMO

BACKGROUND: Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniques. In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. METHODS: We used two datasets: (1) A large dataset (N = 193,311) of high quality clinical chest radiographs, and (2) a dataset (N = 1625) of 3D abdominal computed tomography (CT) images, with the task of classifying the presence of pancreatic ductal adenocarcinoma (PDAC). Both were retrospectively collected and manually labeled by experienced radiologists. We then compared non-private deep convolutional neural networks (CNNs) and privacy-preserving (DP) models with respect to privacy-utility trade-offs measured as area under the receiver operating characteristic curve (AUROC), and privacy-fairness trade-offs, measured as Pearson's r or Statistical Parity Difference. RESULTS: We find that, while the privacy-preserving training yields lower accuracy, it largely does not amplify discrimination against age, sex or co-morbidity. However, we find an indication that difficult diagnoses and subgroups suffer stronger performance hits in private training. CONCLUSIONS: Our study shows that - under the challenging realistic circumstances of a real-life clinical dataset - the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.


Artificial intelligence (AI), in which computers can learn to do tasks that normally require human intelligence, is particularly useful in medical imaging. However, AI should be used in a way that preserves patient privacy. We explored the balance between maintaining patient data privacy and AI performance in medical imaging. We use an approach called differential privacy to protect the privacy of patients' images. We show that, although training AI with differential privacy leads to a slight decrease in accuracy, it does not substantially increase bias against different age groups, genders, or patients with multiple health conditions. However, we notice that AI faces more challenges in accurately diagnosing complex cases and specific subgroups when trained under these privacy constraints. These findings highlight the importance of designing AI systems that are both privacy-conscious and capable of reliable diagnoses across patient groups.

15.
Nat Med ; 2024 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-38965432

RESUMO

Clinical decision-making is one of the most impactful parts of a physician's responsibilities and stands to benefit greatly from artificial intelligence solutions and large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail to assess many skills necessary for deployment in a realistic clinical decision-making environment, including gathering information, adhering to guidelines, and integrating into clinical workflows. Here we have created a curated dataset based on the Medical Information Mart for Intensive Care database spanning 2,400 real patient cases and four common abdominal pathologies as well as a framework to simulate a realistic clinical setting. We show that current state-of-the-art LLMs do not accurately diagnose patients across all pathologies (performing significantly worse than physicians), follow neither diagnostic nor treatment guidelines, and cannot interpret laboratory results, thus posing a serious risk to the health of patients. Furthermore, we move beyond diagnostic accuracy and demonstrate that they cannot be easily integrated into existing workflows because they often fail to follow instructions and are sensitive to both the quantity and order of information. Overall, our analysis reveals that LLMs are currently not ready for autonomous clinical decision-making while providing a dataset and framework to guide future studies.

16.
Med Image Anal ; 92: 103059, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38104402

RESUMO

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.


Assuntos
Neoplasias , Radiologia , Humanos , Inteligência Artificial , Aprendizagem , Neoplasias/diagnóstico por imagem , Processamento de Imagem Assistida por Computador
17.
IEEE Trans Pattern Anal Mach Intell ; 45(6): 7308-7318, 2023 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-37015371

RESUMO

Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many machine learning applications such as the analysis of social and medical networks. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs. In this work, we introduce a framework for differential private graph-level classification. Our method is applicable to graph deep learning on multi-graph datasets and relies on differentially private stochastic gradient descent (DP-SGD). We show results on a variety of datasets and evaluate the impact of different GNN architectures and training hyperparameters on model performance for differentially private graph classification, as well as the scalability of the method on a large medical dataset. Our experiments show that DP-SGD can be applied to graph classification tasks with reasonable utility losses. Furthermore, we apply explainability techniques to assess whether similar representations are learned in the private and non-private settings. Our results can also function as robust baselines for future work in this area.

18.
Lancet Digit Health ; 5(11): e840-e847, 2023 11.
Artigo em Inglês | MEDLINE | ID: mdl-37741765

RESUMO

The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic health data for research and development. Although this planned legislation is undoubtedly a step in the right direction, implementation approaches could potentially result in centralised data silos that pose data privacy and security risks for individuals. To address this concern, we propose federated personal health data spaces, a novel architecture for storing, managing, and sharing personal electronic health records that puts citizens at the centre-both conceptually and technologically. The proposed architecture puts citizens in control by storing personal health data on a combination of personal devices rather than in centralised data silos. We describe how this federated architecture fits within the EHDS and can enable the same features as centralised systems while protecting the privacy of citizens. We further argue that increased privacy and control do not contradict the use of electronic health data for research and development. Instead, data sovereignty and transparency encourage active participation in studies and data sharing. This combination of privacy-by-design and transparent, privacy-preserving data sharing can enable health-care leaders to break the privacy-exploitation barrier, which currently limits the secondary use of health data in many cases.


Assuntos
Registros Eletrônicos de Saúde , Médicos , Humanos , Segurança Computacional , Privacidade , Atenção à Saúde
19.
Cancer Cell ; 41(8): 1498-1515.e10, 2023 08 14.
Artigo em Inglês | MEDLINE | ID: mdl-37451271

RESUMO

Type 1 conventional dendritic cells (cDC1) can support T cell responses within tumors but whether this determines protective versus ineffective anti-cancer immunity is poorly understood. Here, we use imaging-based deep learning to identify intratumoral cDC1-CD8+ T cell clustering as a unique feature of protective anti-cancer immunity. These clusters form selectively in stromal tumor regions and constitute niches in which cDC1 activate TCF1+ stem-like CD8+ T cells. We identify a distinct population of immunostimulatory CCR7neg cDC1 that produce CXCL9 to promote cluster formation and cross-present tumor antigens within these niches, which is required for intratumoral CD8+ T cell differentiation and expansion and promotes cancer immune control. Similarly, in human cancers, CCR7neg cDC1 interact with CD8+ T cells in clusters and are associated with patient survival. Our findings reveal an intratumoral phase of the anti-cancer T cell response orchestrated by tumor-residing cDC1 that determines protective versus ineffective immunity and could be exploited for cancer therapy.


Assuntos
Linfócitos T CD8-Positivos , Neoplasias , Humanos , Receptores CCR7/metabolismo , Neoplasias/terapia , Antígenos de Neoplasias , Células Dendríticas
20.
Med Image Anal ; 84: 102680, 2023 02.
Artigo em Inglês | MEDLINE | ID: mdl-36481607

RESUMO

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.


Assuntos
Benchmarking , Neoplasias Hepáticas , Humanos , Estudos Retrospectivos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/patologia , Fígado/diagnóstico por imagem , Fígado/patologia , Algoritmos , Processamento de Imagem Assistida por Computador/métodos
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