Summary Polycystic liver disease (PLD) is a rare genetic disorder with progressive cyst growth as the primary phenotype. Therapy consists of volume reduction through invasive surgical or radiological procedures. To understand the process of treatment decision, our aim was to identify factors that increased the likelihood of treatment. We performed a cross‐sectional study using an international population of patients with PLD. We collected data on the following therapies: liver transplantation, resection, fenestration, and aspiration sclerotherapy. Data on the potential determinants, sex, center, autosomal dominant polycystic kidney disease (ADPKD), autosomal dominant polycystic liver disease (ADPLD), age at diagnosis, symptoms, and phenotype, were included. We corrected for follow‐up time. We included 578 patients in our study, and 35% underwent invasive therapy. Multivariate regression analysis showed that number of symptoms and age at diagnosis of PLD increased the likelihood of treatment (respectively, RR: 1.4, P < 0.001 and RR = 1.4, P = 0.03). The choice for liver transplantation or aspiration sclerotherapy was center dependent (RR: 0.7, P < 0.001 and RR: 1.1, P = 0.03, respectively). The results of our international cross‐sectional study suggest that a higher number of symptoms and every 10 years of PLD diagnosis increase the risk to undergo treatment by 40%. The choice to elect a particular modality is center dependent.

Introduction: Developments in enhanced and magnified endoscopy have signified major advances in endoscopic imaging of ileocolonic pathology in inflammatory bowel disease (IBD). Artificial intelligence is increasingly being used to augment the benefits of these advanced techniques. Nevertheless, treatment of IBD patients is frustrated by high rates of non-response to therapy, while delayed detection and failures to detect neoplastic lesions impede successful surveillance. A possible solution is offered by molecular imaging, which adds functional imaging data to mucosal morphology assessment through visualizing biological parameters. Other label-free modalities enable visualization beyond the mucosal surface without the need of tracers. Areas covered: A literature search up to May 2020 was conducted in PubMed/MEDLINE in order to find relevant articles that involve the (pre-)clinical application of high-definition white light endoscopy, chromoendoscopy, artificial intelligence, confocal laser endomicroscopy, endocytoscopy, molecular imaging, optical coherence tomography, and Raman spectroscopy in IBD. Expert opinion: Enhanced and magnified endoscopy have enabled an improved assessment of the ileocolonic mucosa. Implementing molecular imaging in endoscopy could overcome the remaining clinical challenges by giving practitioners a real-time in vivo view of targeted biomarkers. Label-free modalities could help optimize the endoscopic assessment of mucosal healing and dysplasia detection in IBD patients.

Optical biopsy in Barrett’s oesophagus (BE) using endocytoscopy (EC) could optimize endoscopic screening. However, the identification of dysplasia is challenging due to the complex interpretation of the highly detailed images. Therefore, we assessed whether using artificial intelligence (AI) as second assessor could help gastroenterologists in interpreting endocytoscopic BE images. First, we prospectively videotaped 52 BE patients with EC. Then we trained and tested the AI pm distinct datasets drawn from 83,277 frames, developed an endocytoscopic BE classification system, and designed online training and testing modules. We invited two successive cohorts for these online modules: 10 endoscopists to validate the classification system and 12 gastroenterologists to evaluate AI as second assessor by providing six of them with the option to request AI assistance. Training the endoscopists in the classification system established an improved sensitivity of 90.0% (+32.67%, p < 0.001) and an accuracy of 77.67% (+13.0%, p = 0.020) compared with the baseline. However, these values deteriorated at follow-up (−16.67%, p < 0.001 and -8.0%, p = 0.009). Contrastingly, AI-assisted gastroenterologists maintained high sensitivity and accuracy at follow-up, subsequently outperforming the unassisted gastroenterologists (+20.0%, p = 0.025 and +12.22%, p = 0.05). Thus, best diagnostic scores for the identification of dysplasia emerged through human–machine collaboration between trained gastroenterologists with AI as the second assessor. Therefore, AI could support clinical implementation of optical biopsies through EC.

The incidence of asymptomatic coeliac-related deficiencies or autoimmune diseases is low in patients with normal values at diagnosis. Therefore, routine laboratory screening is not necessary in this group: attending regular follow-up visits should be sufficient. DEXA scans are recommended.