Validation of AI-based Information Systems for Sensitive Use Cases: Using an XAI Approach in Pharmaceutical Engineering

Artificial Intelligence (AI) is adopted in many businesses. However, adoption lacks behind for use cases with regulatory or compliance requirements, as validation and auditing of AI is still unresolved. AI's opaqueness (i.e., "black box") makes the validation challenging for auditors. Explainable AI (XAI) is the proposed technical countermeasure that can support validation and auditing of AI. We developed an XAI based validation approach for AI in sensitive use cases that facilitates the understanding of the system's behaviour. We conducted a case study in pharmaceutical manufacturing where strict regulatory requirements are present. The validation approach and an XAI prototype were developed through multiple workshops and was then tested and evaluated with interviews. Our approach proved suitable to collect the required evidence for a software validation, but requires additional efforts compared to a traditional software validation. AI validation is an iterative process and clear regulations and guidelines are needed

Changes of health-related quality of life within the 1st year after stroke – results from a prospective stroke cohort study

Introduction: As prospective data on long-term patient-reported outcome measures (PROMs) to assess Health related Quality of Life (HRQoL) after stroke are still scarce, this study examined the long-term course of PROMs and investigated influential factors such as recanalization therapies. Materials and Methods: A total of 945 (mean age 69 years; 56% male) stroke patients were enrolled with a personal interview and chart review performed at index event. One hundred forty (15%) patients received thrombolysis (IVT) and 53 (5%) patients received endovascular therapy (ET) or both treatments as bridging therapy (BT). After 3 and 12 months, a follow-up was conducted using a postal questionnaire including subjective quality of life EQ-5D-5L (European Quality of Life 5 Dimensions). At all time-points, Modified Rankin Scale (mRS) was additionally used to quantify functional stroke severity. Differences between therapy groups were identified using post-hoc-tests. Linear and logistic regression analyses were used to identify predictors of outcomes. Results: Recanalization therapies were associated with significant improvements of NIHSS (National Institutes of Health Stroke Scale [regression coefficient IVT 1.21 (p = 0.01) and ET/BT 7.6; p = 0.001] and mRS (modified Rankin Scale) [regression coefficient IVT 0.83; p = 0.001 and ET/BT 2.0; p = 0.001] between admission and discharge compared to patients with stroke unit therapy only, with a trend toward improvement of EQ-5D after 12 months [regression coefficient 4.67 (p = 0.17)] with IVT. HRQoL was considerably impaired by stroke and increased steadily in 3- and 12-months follow-up in patients with (mean EQ-5D from 56 to 68) and without recanalization therapy (mean EQ-5D from 62 to 68). In severe strokes a major and significant improvement was only detected during period of 3 to 12 months (p = 0.03 in patients with and p = 0.005 in patients without recanalization therapy). Conclusions: Despite significant and continuous improvements after stroke the HRQoL after 12 months remained below the age-matched general population, but was still unexpectedly high in view of the accumulation of permanent disabilities in up to 30% of the patients. Especially in severe strokes, it is important to evaluate HRQoL beyond a 3-months follow-up as improvements became significant only between 3 months and 1 year

Collaborative analysis of multi-gigapixel imaging data using Cytomine

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications

Diminished impact of cytomegalovirus infection on graft vasculopathy development in the antiviral prophylaxis era – a retrospective study

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Analysis of Osteosarcoma Cell Lines and Patient Tissue Using a 3D In Vivo Tumor Model—Possible Effects of Punicalagin

Osteosarcomas are the most common primary malignant bone tumors and mostly affect children, adolescents, and young adults. Despite current treatment options such as surgery and polychemotherapy, the survival of patients with metastatic disease remains poor. In recent studies, punicalagin has reduced the cell viability, angiogenesis, and invasion in cell culture trials. The aim of this study was to examine the effects of punicalagin on osteosarcomas in a 3D in vivo tumor model. Human osteosarcoma biopsies and SaOs-2 and MG-63 cells, were grown in a 3D in vivo chorioallantoic membrane (CAM) model. After a cultivation period of up to 72 h, the tumors received daily treatment with punicalagin for 4 days. Weight measurements of the CAM tumors were performed, and laser speckle contrast imaging (LSCI) and a deep learning-based image analysis software (CAM Assay Application v.3.1.0) were used to measure angiogenesis. HE, Ki-67, and Caspase-3 staining was performed after explantation. The osteosarcoma cell lines SaOs-2 and MG-63 and osteosarcoma patient tissue displayed satisfactory growth patterns on the CAM. Treatment with punicalagin decreased tumor weight, proliferation, and tumor-induced angiogenesis, and the tumor tissue showed pro-apoptotic characteristics. These results provide a robust foundation for the implementation of further studies and show that punicalagin offers a promising supplementary treatment option for osteosarcoma patients. The 3D in vivo tumor model represents a beneficial model for the testing of anti-cancer therapies

Subcutaneous fat patterning in athletes: selection of appropriate sites and standardisation of a novel ultrasound measurement technique: ad hoc working group on body composition, health and performance, under the auspices of the IOC Medical Commission.

Background: Precise and accurate field methods for body composition analyses in athletes are needed urgently. Aim: Standardisation of a novel ultrasound (US) technique for accurate and reliable measurement of subcutaneous adipose tissue (SAT). Methods: Three observers captured US images of uncompressed SAT in 12 athletes and applied a semiautomatic evaluation algorithm for multiple SAT measurements. Results: Eight new sites are recommended: upper abdomen, lower abdomen, erector spinae, distal triceps, brachioradialis, lateral thigh, front thigh, medial calf. Obtainable accuracy was 0.2 mm (18 MHz probe; speed of sound: 1450 m/s). Reliability of SAT thickness sums (N=36): R2=0.998, SEE=0.55 mm, ICC (95% CI) 0.998 (0.994 to 0.999); observer differences from their mean: 95% of the SAT thickness sums were within ±1 mm (sums of SAT thicknesses ranged from 10 to 50 mm). Embedded fibrous tissues were also measured. Conclusions: A minimum of eight sites is suggested to accommodate inter-individual differences in SAT patterning. All sites overlie muscle with a clearly visible fascia, which eases the acquisition of clear images and the marking of these sites takes only a few minutes. This US method reaches the fundamental accuracy and precision limits for SAT measurements given by tissue plasticity and furrowed borders, provided the measurers are trained appropriately

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.ISSN:2167-835