Diabetic foot ulcer (DFU) is a chronic wound and a common diabetic complication as 2% – 6% of diabetic patients witness the onset thereof. The DFU can lead to severe health threats such as infection and lower leg amputations, Coordination of interdisciplinary wound care requires well-written but time-consuming wound documentation. Artificial intelligence (AI) systems lend themselves to be tested to extract information from wound images, e.g. maceration, to fill the wound documentation. A convolutional neural network was therefore trained on 326 augmented DFU images to distinguish macerated from unmacerated wounds. The system was validated on 108 unaugmented images. The classification system achieved a recall of 0.69 and a precision of 0.67. The overall accuracy was 0.69. The results show that AI systems can classify DFU images for macerations and that those systems could support clinicians with data entry. However, the validation statistics should be further improved for use in real clinical settings. In summary, this paper can contribute to the development of methods to automatic wound documentation.

Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

Introduction: The interaction between nurses and physicians in the primary care setting is challenging with regard to structural, process and technical barriers. In order to overcome these barriers, the eMedCare project was launched and a commercial system was implemented. Objective: This study aimed at a formative evaluation of the project. The findings should be used retrospectively to understand the failure of the project. Methods: To this end, two rounds of qualitative interviews with 10 respectively 8 healthcare providers were performed. Results: The interviews revealed a mixed benefit. Difficulties arose because the initial aim to monitor patients shifted towards improving the communication between the providers, partly due to the poor usability of the monitoring system. Additional workload was imposed because the system was not interoperable with the institutional IT systems. Conclusion: Projects with an unclear or shifting vision and focus seem to be susceptible to failure. The secure communication applications could have been realised on the intended scale if the national Telematikinfrastruktur had been in place.

Pyoderma gangrenosum (PG) is a non‐infectious, neutrophilic dermatosis that was difficult to diagnose in clinical practice. Today, the PARACELSUS score is a validated tool for diagnostics. Based on this score, patients with clearly diagnosed PG were examined with regard to predilection sites. In this retrospective study, the data of patients from the University Hospitals of Essen and Erlangen were analysed in whom the diagnosis of PG could be clearly confirmed using the PARACELSUS score. A total of 170 patients, 49 men (29%) and 121 women (71%) with an average age at first manifestation of 55.5 years, could be included in the analysis. The predilection sites were identified as the lower legs in 80.6% of the patients and the extensor sides in 75.2%. Other localisations of PG were the thighs in 14.1%, mammae and abdomen in 10.0% each, back and gluteal in 7.1% each, feet in 5.9%, arms in 4.7%, genital in 3.5% and head in 2.9%. This retrospective study is the first to identify a collective of PG patients with the highest data quality using the PARACELSUS score. It could be shown that PG can basically occur on the entire integument. However, the predilection sites of PG, which have now been reliably identified for the first time, are the lower legs and in particular the extensor sides.