Background: After the outbreak of the corona virus disease-19 (COVID-19) pandemic, teledermatology was implemented in the Hungarian public healthcare system for the first time. Our objective was to assess aggregated diagnostic agreements and to determine the effectiveness of an asynchronous teledermatology system for skin cancer screening. Methods: This retrospective single-center study included cases submitted for teledermatology consultation during the first wave of the COVID-19 pandemic. Follow-up of the patients was performed to collect the results of any subsequent personal examination. Results: 749 patients with 779 lesions were involved. 15 malignant melanomas (9.9%), 78 basal cell carcinomas (51.3%), 21 squamous cell carcinomas (13.8%), 7 other malignancies (4.6%) and 31 actinic keratoses (20.4%) were confirmed. 87 malignancies were diagnosed in the high-urgency group (42.2%), 49 malignancies in the moderate-urgency group (21.6%) and 16 malignancies in the low-urgency group (4.6%) (p < 0.0001). Agreement of malignancies was substantial for primary (86.3%; κ = 0.647) and aggregated diagnoses (85.3%; κ = 0.644). Agreement of total lesions was also substantial for primary (81.2%; κ = 0.769) and aggregated diagnoses (87.9%; κ = 0.754). Conclusions: Our findings showed that asynchronous teledermatology using a mobile phone application served as an accurate skin cancer screening system during the first wave of the COVID-19 pandemic.

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Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions.

Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.

There is a growing scope of different telemedicine modalities in patient care, especially with the development of various communication and digital imaging devices. In dermatology, there has been an unprecedented increase in the number of healthcare facilities providing teledermatology consultations after the outbreak of the COVID-19 pandemic. Experiences have shown that the application of teledermatology systems can increase access to dermatology care for the population, reduce waiting time for cases requiring immediate face-to-face consultation and minimize costs for patients and, in the longer term, for healthcare systems as well.