Piotr Foltynski

Background: As a standalone parameter, the wound surface area can be used to describe a wound in medical records; however, changes in the wound surface area over time can be used in chronic wounds to assess treatment efficacy and predict successful healing. Recent technological advances in mobile devices and artificial intelligence algorithms have enabled the development of new methods for measuring the surface area of wounds. These new methods require clinical validation or comparison with previously used methods. Methods: Wound surface areas were measured with the Planimator app (PA), Silhouette Mobile device (SMD), and AutoPlanimator service (APS) in 77 patients from the outpatient clinic. One hundred forty-two ulcers were measured. The Passing-Bablok regression analysis was used to assess differences between each pair of methods used. Results and Conclusion: There were significant differences in proportional differences between two pairs of methods, ie, between the SMD and the PA and between the SMD and the APS. The Passing-Bablok regression analysis revealed the regression equation APS = −0.0145 + 0.987PA for the APS and the PA. There were nonsignificant differences between systematic and proportional differences for the PA and the APS; thus, the inequality between the APS and the PA cannot be confirmed and the two methods can be interchangeably used, which cannot be concluded for the SMD and the APS or the SMD and the PA. This study confirmed that the PA and the APS can be valuable tools in assessing the healing progress of chronic wounds.

Purpose: Optimal glycemic control is crucial for proper wound healing in patients with diabetes. However, it is not clear whether other antidiabetic drugs support wound healing in mechanisms different from the normalization of blood glucose control. We assessed the effect of insulin and metformin administration on the wound healing process in rats with streptozotocin-induced diabetes. Methods: The study was conducted on 200 male Wistar rats with streptozotocin-induced diabetes. In the last phase of the study, 45 rats, with the most stable glucose levels in the range of 350– 500 mg/dL, were divided into three groups: group I received human non-protamine insulin subcutaneously (5 IU/kg body mass) once a day, group II received metformin intragastrically (500 mg/kg b.m.), and group III (control) was given saline subcutaneously. After 14 days of antidiabetic treatment, a 2 cm × 2 cm thin layer of skin was cut from each rat’s dorsum and a 4 cm disk with a hole in its center was sewn in to stabilize the skin and standardize the healing process. The wound healing process was followed up for 9 days, with assessment every 3 days. Biopsy samples were subjected to hematoxylin and eosin staining and immunohistochemical assays. Results: Analysis of variance revealed significant influence of treatment type (insulin, control, or metformin) on the relative change in wound surface area. The wound healing process in rats treated with insulin was more effective than in the metformin and control groups. Wound tissue samples taken from the insulin-treated animals presented significantly lower levels of inflammatory infiltration. Immunohistochemical assessment showed the greatest density of centers of proliferation Ki-67 in insulin-treated animals. Conclusion: These results suggest that an insulin-based treatment is more beneficial than metformin, in terms of accelerating the wound healing process in an animal model of streptozocin-induced diabetes.

Background: The purpose of this study was to determine the accuracy of wound area measurement at a curved surface using a digital planimetry (DP) with the newly proposed adaptive calibration. Methods: Forty wound shapes were printed and placed at the side surfaces of cylinders with diameters of 9.4 and 6.2 cm. Area measurements were carried out using a commercial device SilhouetteMobile (Aranz, New Zealand) and the planimetric app Planimator. Planimetric area measurements were carried out using 2 one-dimensional calibration markers placed above and below the wound shape. The method of adaptive calibration for DP was described. Reference area values of wound shapes were obtained by pixel counting on digital scans made with an optical scanner. Relative errors (REs) and relative differences (RDs) for area measurements were analyzed. Results: The median of REs for the DP with adaptive calibration (DPwAC) was equal to 0.60% and was significantly smaller than the median for the SilhouetteMobile device (SMD) (2.65%), and significantly smaller than the median for the DP (2.23%). The SD of RDs for the DPwAC of 0.87% was considerably lower than for the SMD (6.45%), and for the DP without adaptive calibration (2.51%). The mean of RDs for the DPwAC (0.082%) was not significantly different from zero, which means that the systematic error was not present for the DPwAC. Conclusions: The use of the adaptive calibration in DP to measure the areas at curved surface resulted in a significant increase of accuracy and precision, and removal of systematic error. The DPwAC revealed 4.4 times lower error and 7.4 times higher precision of area measurement at curved surfaces than the SMD.

The aim of this work was to assess the accuracy of automatic macronutrient and calorie counting based on voice descriptions of meals provided by people with unstable type 1 diabetes using the developed expert system (VoiceDiab) in comparison with reference counting made by a dietitian, and to evaluate the impact of insulin doses recommended by a physician on glycemic control in the study’s participants. We also compared insulin doses calculated using the algorithm implemented in the VoiceDiab system. Meal descriptions were provided by 30 hospitalized patients (mean hemoglobin A1c of 8.4%, i.e., 68 mmol/mol). In 16 subjects, the physician determined insulin boluses based on the data provided by the system, and in 14 subjects, by data provided by the dietitian. On one hand, differences introduced by patients who subjectively described their meals compared to those introduced by the system that used the average characteristics of food products, although statistically significant, were low enough not to have a significant impact on insulin doses automatically calculated by the system. On the other hand, the glycemic control of patients was comparable regardless of whether the physician was using the system-estimated or the reference content of meals to determine insulin doses.

Wound surface area measurement is important as therapeutic decisions may depend on the change of wound surface area over time. Digital planimetry is a popular technique in wound area measurement. It is accurate and repeatable when calibration is performed with 2 rulers placed at opposite sides of a wound. The aim of the current study was improving accuracy and precision of wound area measurement using capabilities of smart devices. The correction of area measurement based on calculated camera tilt angle and the calculation of calibration coefficient of linear dimensions as weighted average were proposed. These and other improvements were applied in the Planimator app for Android, which was then used in the study. Accuracy and precision of the Planimator app were compared to the Visitrak device, the SilhouetteMobile device, the AreaMe software, and to the digital planimetry based on 2-ruler calibration with pictures taken by the smartphone, compact, and D-SLR cameras. Areas of 40 wound shapes of area ranged from 0.14 to 31.72 cm² were measured with each device. Medians of relative errors (REs) were compared in the accuracy tests and standard deviations (SDs) of relative differences (RDs) were compared in the tests of precision. The median of REs for the Planimator app was not significantly different from the medians of REs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the medians of REs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The SD of RDs for the Planimator app was not significantly different from the SDs of RDs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the SDs of RDs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The Planimator app installed at a smartphone revealed to be 2-fold more accurate and 1.5-fold more precise than the measurements with using ImageJ software based on pictures taken with the same smartphone. The Planimator app occurred to have the same accuracy and precision as measurements with digital planimetry with 2-ruler calibration and based on pictures from a compact camera or a D-SLR camera. This app showed better accuracy and precision than the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera.

Background: Patients using an insulin pump as part of their diabetes treatment need to calculate insulin bolus doses to compensate for a meal. Some patients do not modify their meal boluses according to changes in the amount and composition of food products in a meal. The lack of correct meal boluses leads to unstable, and therefore harmful, blood glucose levels. The aim of the present study was to test a system supporting bolus determination based on a voice description of a meal.Methods: The bolus calculator developed (VoiceDiab) consists of a smartphone application and three remote servers for automatic speech recognition, text analysis, and insulin dosage calculation. Forty‐four people with type 1 diabetes (T1D) treated with continuous subcutaneous insulin infusion finished the randomized cross‐over study. Patients were randomly allocated to the group in which the VoiceDiab system supported bolus calculation or to an unsupported group, in which patients or their caregivers calculated boluses. After a 14‐day washout period, patients from the supported group were switched to the unsupported group, whereas those in the unsupported group were switched to the supported group.Results: There was a significant difference between the supported and unsupported groups in the percentage of patients with 2‐h postprandial glycemia within the 70–180 mg/dL range (58.6% vs 46.6%, respectively;P= 0.031).Conclusions: The VoiceDiab system improves postprandial glucose control without increasing the time in hyperglycemia or hypoglycemia. Therefore, it may be useful in the treatment of patients with diabetes on intensive insulin therapy.