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Journal and Magazine Articles

Patient-Centered Chronic Wound Care Mobile Apps: Systematic Identification, Analysis, and Assessment. Dege, Tassilo; Glatzel, Bernadette; Borst, Vanessa; Grän, Franziska; Goller, Simon; Glatzel, Caroline; Goebeler, Matthias; Schmieder, Astrid; in JMIR mHealth and uHealth (2024). 12
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Background: The prevalence of chronic wounds is predicted to increase within the aging populations in industrialized countries. Patients experience significant distress due to pain, wound secretions, and the resulting immobilization. As the number of wounds continues to rise, their adequate care becomes increasingly costly in terms of health care resources worldwide. eHealth support systems are being increasingly integrated into patient care. However, to date, no systematic analysis of such apps for chronic wounds has been published. Objective: The aims of this study were to systematically identify and subjectively assess publicly available German- or English-language mobile apps for patients with chronic wounds, with quality assessments performed by both patients and physicians. Methods: Two reviewers independently conducted a systematic search and assessment of German- or English-language mobile apps for patients with chronic wounds that were available in the Google Play Store and Apple App Store from April 2022 to May 2022. In total, 3 apps met the inclusion and exclusion criteria and were reviewed independently by 10 physicians using the German Mobile App Rating Scale (MARS) and the System Usability Scale (SUS). The app with the highest mean MARS score was subsequently reviewed by 11 patients with chronic wounds using the German user version of the MARS (uMARS) and the SUS. Additionally, Affinity for Technology Interaction (ATI) scale scores were collected from both patients and physicians. Results: This study assessed mobile apps for patients with chronic wounds that were selected from a pool of 118 identified apps. Of the 73 apps available in both app stores, 10 were patient oriented. After excluding apps with advertisements or costs, 3 apps were evaluated by 10 physicians. Mean MARS scores ranged from 2.64 (SD 0.65) to 3.88 (SD 0.65) out of 5, and mean SUS scores ranged from 50.75 (SD 27) to 80.5 (SD 17.7) out of 100. WUND APP received the highest mean MARS score (mean 3.88, SD 0.65 out of 5) among physicians. Hence, it was subsequently assessed by 11 patients and achieved a similar rating (uMARS score: mean 3.89, SD 0.4 out of 5). Technical affinity, as measured with the ATI scale, was slightly lower in patients (score: mean 3.62, SD 1.35 out of 6) compared to physicians (score: mean 3.88, SD 1.03 out 6). Conclusions: The quality ratings from physicians and patients were comparable and indicated mediocre app quality. Technical affinity, as assessed by using the ATI scale, was slightly lower for patients. Adequate apps for patients with chronic wounds remain limited, emphasizing the need for improved app development to meet patient needs. The ATI scale proved valuable for assessing technical affinity among different user groups.

@article{dege2024patientcentered, abstract = {Background: The prevalence of chronic wounds is predicted to increase within the aging populations in industrialized countries. Patients experience significant distress due to pain, wound secretions, and the resulting immobilization. As the number of wounds continues to rise, their adequate care becomes increasingly costly in terms of health care resources worldwide. eHealth support systems are being increasingly integrated into patient care. However, to date, no systematic analysis of such apps for chronic wounds has been published. Objective: The aims of this study were to systematically identify and subjectively assess publicly available German- or English-language mobile apps for patients with chronic wounds, with quality assessments performed by both patients and physicians. Methods: Two reviewers independently conducted a systematic search and assessment of German- or English-language mobile apps for patients with chronic wounds that were available in the Google Play Store and Apple App Store from April 2022 to May 2022. In total, 3 apps met the inclusion and exclusion criteria and were reviewed independently by 10 physicians using the German Mobile App Rating Scale (MARS) and the System Usability Scale (SUS). The app with the highest mean MARS score was subsequently reviewed by 11 patients with chronic wounds using the German user version of the MARS (uMARS) and the SUS. Additionally, Affinity for Technology Interaction (ATI) scale scores were collected from both patients and physicians. Results: This study assessed mobile apps for patients with chronic wounds that were selected from a pool of 118 identified apps. Of the 73 apps available in both app stores, 10 were patient oriented. After excluding apps with advertisements or costs, 3 apps were evaluated by 10 physicians. Mean MARS scores ranged from 2.64 (SD 0.65) to 3.88 (SD 0.65) out of 5, and mean SUS scores ranged from 50.75 (SD 27) to 80.5 (SD 17.7) out of 100. WUND APP received the highest mean MARS score (mean 3.88, SD 0.65 out of 5) among physicians. Hence, it was subsequently assessed by 11 patients and achieved a similar rating (uMARS score: mean 3.89, SD 0.4 out of 5). Technical affinity, as measured with the ATI scale, was slightly lower in patients (score: mean 3.62, SD 1.35 out of 6) compared to physicians (score: mean 3.88, SD 1.03 out 6). Conclusions: The quality ratings from physicians and patients were comparable and indicated mediocre app quality. Technical affinity, as assessed by using the ATI scale, was slightly lower for patients. Adequate apps for patients with chronic wounds remain limited, emphasizing the need for improved app development to meet patient needs. The ATI scale proved valuable for assessing technical affinity among different user groups.}, author = {Dege, Tassilo and Glatzel, Bernadette and Borst, Vanessa and Grän, Franziska and Goller, Simon and Glatzel, Caroline and Goebeler, Matthias and Schmieder, Astrid}, journal = {JMIR mHealth and uHealth}, keywords = {t_journalmagazine}, month = {03}, title = {Patient-Centered Chronic Wound Care Mobile Apps: Systematic Identification, Analysis, and Assessment}, volume = 12, year = 2024 }
Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery. Müller, Konstantin; Leppich, Robert; Geiß, Christian; Borst, Vanessa; Pelizari, Patrick Aravena; Kounev, Samuel; in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023). 1–14.
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In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time-consuming and costly. Only a few approaches exist to create high-resolution normalized digital surface models for extensive areas. This paper explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep-learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixel-wise regression, our U-Net base models can achieve a mean height error of approximately two meters. Moreover, through our enhancements to the model architecture, we reduce the model error by more than seven percent.

@article{10189905, abstract = {In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time-consuming and costly. Only a few approaches exist to create high-resolution normalized digital surface models for extensive areas. This paper explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep-learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixel-wise regression, our U-Net base models can achieve a mean height error of approximately two meters. Moreover, through our enhancements to the model architecture, we reduce the model error by more than seven percent.}, author = {Müller, Konstantin and Leppich, Robert and Geiß, Christian and Borst, Vanessa and Pelizari, Patrick Aravena and Kounev, Samuel}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, keywords = {t_journalmagazine}, pages = {1-14}, title = {Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery}, year = 2023 }

Full Papers

MACRO: A Multi-Head Attentional Convolutional Recurrent Network for the Classification of Co-Occurring Diseases in 12-Lead ECGs. Borst, Vanessa; Leppich, Robert; Kounev, Samuel; in 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2024). IEEE.

Acceptance Rate: 24.4%
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@inproceedings{borst2024MACRO, author = {Borst, Vanessa and Leppich, Robert and Kounev, Samuel}, booktitle = {2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)}, keywords = {t_interdisciplinary}, note = {Acceptance Rate: 24.4%}, organization = {IEEE}, title = {{MACRO}: A Multi-Head Attentional Convolutional Recurrent Network for the Classification of Co-Occurring Diseases in 12-Lead {ECGs}}, year = 2024 }
Personal Task Design Preferences of Crowdworkers. Hirth, Matthias; Borchert, Kathrin; de Moor, Katrien; Borst, Vanessa; Hoßfeld, Tobias; in 12th International Conference on Quality of Multimedia Experience (QoMEX) (2020). Athlone, Ireland.
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@inproceedings{info3-inproceedings-2020-5, address = {Athlone, Ireland}, author = {Hirth, Matthias and Borchert, Kathrin and de Moor, Katrien and Borst, Vanessa and Hoßfeld, Tobias}, booktitle = {12th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {t_full}, month = {05}, title = {Personal Task Design Preferences of Crowdworkers}, year = 2020 }

Short Papers

Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devices. Borst, Vanessa; Dittus, Timo; Müller, Konstantin; Kounev, Samuel; in 47th German Conference on Artificial Intelligence - KI 2024 (2024).
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@conference{borst2024early, author = {Borst, Vanessa and Dittus, Timo and Müller, Konstantin and Kounev, Samuel}, booktitle = {47th German Conference on Artificial Intelligence - KI 2024}, keywords = {t_interdisciplinary}, title = {Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devices}, year = 2024 }

Posters

Segmentierung und Verlaufskontrolle chronischer Wunden durch künstliche Intelligenz mithilfe einer mobilen App (WUNDERKINT Studie). Borst, Vanessa; Dege, Tassilo; Grän, Franziska; Hamrouni, Sandra; Michel, Daniel; Hager, Valerie; Goebeler, Matthias; Leppich, Robert; Kounev, Samuel; Schmieder, Astrid; in Allergologie. JDDG: Journal der Deutschen Dermatologischen Gesellschaft (2023). (Vol. 21) 28–177. John Wiley & Sons Ltd.

Poster Abstract (No. P322, pp. 175-176)
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@incollection{borst_ddg23_v2, author = {Borst, Vanessa and Dege, Tassilo and Grän, Franziska and Hamrouni, Sandra and Michel, Daniel and Hager, Valerie and Goebeler, Matthias and Leppich, Robert and Kounev, Samuel and Schmieder, Astrid}, booktitle = {Allergologie. JDDG: Journal der Deutschen Dermatologischen Gesellschaft}, keywords = {t_poster}, month = {04}, note = {Poster Abstract (No. P322, pp. 175-176)}, number = {S1}, pages = {28-177}, publisher = {John Wiley & Sons Ltd.}, title = {Segmentierung und Verlaufskontrolle chronischer Wunden durch künstliche Intelligenz mithilfe einer mobilen App (WUNDERKINT Studie)}, volume = 21, year = 2023 }

Press Coverage

Main-Post (Press Release im Kontext der Verleihung des "Sonderforschungsförderpreises Medizin 2022" für das interdisziplinäre Forschungsprojekt "Wunderkint"): Großes Festkonzert des Universitätsbundes in der Neubaukirche mit Preisverleihung. November 2022. [ Link ]

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