@article{WehrheimFaskowitzSpornsetal.2023, author = {Wehrheim, Maren H. and Faskowitz, Joshua and Sporns, Olaf and Fiebach, Christian J. and Kaschube, Matthias and Hilger, Kirsten}, title = {Few temporally distributed brain connectivity states predict human cognitive abilities}, series = {NeuroImage}, volume = {277}, journal = {NeuroImage}, doi = {10.1016/j.neuroimage.2023.120246}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349874}, year = {2023}, abstract = {Highlights • Brain connectivity states identified by cofluctuation strength. • CMEP as new method to robustly predict human traits from brain imaging data. • Network-identifying connectivity 'events' are not predictive of cognitive ability. • Sixteen temporally independent fMRI time frames allow for significant prediction. • Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths. Abstract Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5\% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.}, language = {en} } @article{KrenzerHeilFittingetal., author = {Krenzer, Adrian and Heil, Stefan and Fitting, Daniel and Matti, Safa and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank}, title = {Automated classification of polyps using deep learning architectures and few-shot learning}, series = {BMC Medical Imaging}, volume = {23}, journal = {BMC Medical Imaging}, doi = {10.1186/s12880-023-01007-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357465}, abstract = {Background Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results For the Paris classification, we achieve an accuracy of 89.35 \%, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 \% and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.}, language = {en} } @article{BeierlePryssAizawa2023, author = {Beierle, Felix and Pryss, R{\"u}diger and Aizawa, Akiko}, title = {Sentiments about mental health on Twitter — before and during the COVID-19 pandemic}, series = {Healthcare}, volume = {11}, journal = {Healthcare}, number = {21}, issn = {2227-9032}, doi = {10.3390/healthcare11212893}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-355192}, year = {2023}, abstract = {During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags \#MentalHealth and \#Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags \#Depression and especially \#MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with \#MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81\% for \#MentalHealth and 79\% for \#Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.}, language = {en} } @article{GriebelSegebarthSteinetal.2023, author = {Griebel, Matthias and Segebarth, Dennis and Stein, Nikolai and Schukraft, Nina and Tovote, Philip and Blum, Robert and Flath, Christoph M.}, title = {Deep learning-enabled segmentation of ambiguous bioimages with deepflash2}, series = {Nature Communications}, volume = {14}, journal = {Nature Communications}, doi = {10.1038/s41467-023-36960-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357286}, year = {2023}, abstract = {Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool's training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.}, language = {en} } @article{VollmerNaglerHoerneretal.2023, author = {Vollmer, Andreas and Nagler, Simon and H{\"o}rner, Marius and Hartmann, Stefan and Brands, Roman C. and Breitenb{\"u}cher, Niko and Straub, Anton and K{\"u}bler, Alexander and Vollmer, Michael and Gubik, Sebastian and Lang, Gernot and Wollborn, Jakob and Saravi, Babak}, title = {Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery}, series = {Heliyon}, volume = {9}, journal = {Heliyon}, number = {11}, issn = {2405-8440}, doi = {10.1016/j.heliyon.2023.e20752}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350416}, year = {2023}, abstract = {Background Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.}, language = {en} } @article{CaliskanCaliskanRasbachetal.2023, author = {Caliskan, Aylin and Caliskan, Deniz and Rasbach, Lauritz and Yu, Weimeng and Dandekar, Thomas and Breitenbach, Tim}, title = {Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning}, series = {Computational and Structural Biotechnology Journal}, volume = {21}, journal = {Computational and Structural Biotechnology Journal}, issn = {2001-0370}, doi = {10.1016/j.csbj.2023.06.002}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349989}, pages = {3293-3314}, year = {2023}, abstract = {Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.}, language = {en} } @article{DresiaKurudzijaDeekenetal.2023, author = {Dresia, Kai and Kurudzija, Eldin and Deeken, Jan and Waxenegger-Wilfing, G{\"u}nther}, title = {Improved wall temperature prediction for the LUMEN rocket combustion chamber with neural networks}, series = {Aerospace}, volume = {10}, journal = {Aerospace}, number = {5}, issn = {2226-4310}, doi = {10.3390/aerospace10050450}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319169}, year = {2023}, abstract = {Accurate calculations of the heat transfer and the resulting maximum wall temperature are essential for the optimal design of reliable and efficient regenerative cooling systems. However, predicting the heat transfer of supercritical methane flowing in cooling channels of a regeneratively cooled rocket combustor presents a significant challenge. High-fidelity CFD calculations provide sufficient accuracy but are computationally too expensive to be used within elaborate design optimization routines. In a previous work it has been shown that a surrogate model based on neural networks is able to predict the maximum wall temperature along straight cooling channels with convincing precision when trained with data from CFD simulations for simple cooling channel segments. In this paper, the methodology is extended to cooling channels with curvature. The predictions of the extended model are tested against CFD simulations with different boundary conditions for the representative LUMEN combustor contour with varying geometries and heat flux densities. The high accuracy of the extended model's predictions, suggests that it will be a valuable tool for designing and analyzing regenerative cooling systems with greater efficiency and effectiveness.}, language = {en} } @article{HaufeIsaiasPellegrinietal.2023, author = {Haufe, Stefan and Isaias, Ioannis U. and Pellegrini, Franziska and Palmisano, Chiara}, title = {Gait event prediction using surface electromyography in parkinsonian patients}, series = {Bioengineering}, volume = {10}, journal = {Bioengineering}, number = {2}, issn = {2306-5354}, doi = {10.3390/bioengineering10020212}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304380}, year = {2023}, abstract = {Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2\%), and next to no false-positive event detections (<0.1\%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.}, language = {en} } @article{KunzStellzigEisenhauerBoldt2023, author = {Kunz, Felix and Stellzig-Eisenhauer, Angelika and Boldt, Julian}, title = {Applications of artificial intelligence in orthodontics — an overview and perspective based on the current state of the art}, series = {Applied Sciences}, volume = {13}, journal = {Applied Sciences}, number = {6}, issn = {2076-3417}, doi = {10.3390/app13063850}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-310940}, year = {2023}, abstract = {Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.}, language = {en} } @article{HenckertMalorgioSchweigeretal.2023, author = {Henckert, David and Malorgio, Amos and Schweiger, Giovanna and Raimann, Florian J. and Piekarski, Florian and Zacharowski, Kai and Hottenrott, Sebastian and Meybohm, Patrick and Tscholl, David W. and Spahn, Donat R. and Roche, Tadzio R.}, title = {Attitudes of anesthesiologists toward artificial intelligence in anesthesia: a multicenter, mixed qualitative-quantitative study}, series = {Journal of Clinical Medicine}, volume = {12}, journal = {Journal of Clinical Medicine}, number = {6}, issn = {2077-0383}, doi = {10.3390/jcm12062096}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311189}, year = {2023}, abstract = {Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96\%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17\%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44\%) and cons (59 of 105, 56\%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70\%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.}, language = {en} }