Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

Objective: A common source of concern about digital pathology (DP) is that limited resolution could be a reason for an increased risk of malpractice. A frequent question being raised about this technology is whether it can be used to reliably detect Helicobacter pylori (HP) in gastric biopsies, which can be a significant burden in routine work. The main goal of this work is to show that a reliable diagnosis of HP infection can be made by DP even at low magnification. The secondary goal is to demonstrate that artificial intelligence (AI) algorithms can diagnose HP infections on virtual slides with sufficient accuracy. Methods: The method we propose is based on the Warthin-Starry (W-S) silver stain which allows faster detection of HP in virtual slides. A software tool, based on regular expressions, performed a specific search to select 679 biopsies on which a W-S stain was done. From this dataset 185 virtual slides were selected to be assessed by WSI and compared with microscopy slide readings. To determine whether HP infections could be accurately diagnosed with machine learning. AI was used as a service (AIaaS) on a neural network-based web platform trained with 468 images. A test dataset of 210 images was used to assess the classifier performance. Results: In 185 gastric biopsies read with DP we recorded only 4 false positives and 4 false negatives with an overall agreement of 95.6%. Compared with microscopy, defined as the "gold standard" for the diagnosis of HP infections, WSI had a sensitivity and specificity of 0.95 and 0.96, respectively. The ROC curve of our AI classifier generated on a testing dataset of 210 images had an AUC of 0.938. Conclusions: This study demonstrates that DP and AI can be used to reliably identify HP at 20X resolution.

Preventive maintenance prioritization index of medical equipment using quality function deployment.

Preventive maintenance is a core function of clinical engineering, and it is essential to guarantee the correct functioning of the equipment. The management and control of maintenance activities are equally important to perform maintenance. As the variety of medical equipment increases, accordingly the size of maintenance activities increases, the need for better management and control become essential. This paper aims to develop a new model for preventive maintenance priority of medical equipment using quality function deployment as a new concept in maintenance of medical equipment. We developed a three-domain framework model consisting of requirement, function, and concept. The requirement domain is the house of quality matrix. The second domain is the design matrix. Finally, the concept domain generates a prioritization index for preventive maintenance considering the weights of critical criteria. According to the final scores of those criteria, the prioritization action of medical equipment is carried out. Our model proposes five levels of priority for preventive maintenance. The model was tested on 200 pieces of medical equipment belonging to 17 different departments of two hospitals in Piedmont province, Italy. The dataset includes 70 different types of equipment. The results show a high correlation between risk-based criteria and the prioritization list.