Automatic three-dimensional reconstruction of the oesophagus in achalasia patients undergoing POEM: an innovative approach for evaluating treatment outcomes.

BACKGROUND AND AIMS: Peroral endoscopic myotomy (POEM) is a standard treatment option for achalasia patients. Treatment response varies due to factors such as achalasia type, degree of dilatation, pressure and distensibility indices. We present an innovative approach for treatment response prediction based on an automatic three-dimensional (3-D) reconstruction of the tubular oesophagus (TE) and the lower oesophageal sphincter (LES) in patients undergoing POEM for achalasia. METHODS: A software was developed, integrating data from high-resolution manometry, timed barium oesophagogram and endoscopic images to automatically generate 3-D reconstructions of the TE and LES. Novel normative indices for TE (volume×pressure) and LES (volume/pressure) were automatically integrated, facilitating pre-POEM and post-POEM comparisons. Treatment response was evaluated by changes in volumetric and pressure indices for the TE and the LES before as well as 3 and 12 months after POEM. In addition, these values were compared with normal value indices of non-achalasia patients. RESULTS: 50 treatment-naive achalasia patients were enrolled prospectively. The mean TE index decreased significantly (p<0.0001) and the mean LES index increased significantly 3 months post-POEM (p<0.0001). In the 12-month follow-up, no further significant change of value indices between 3 and 12 months post-POEM was seen. 3 months post-POEM mean LES index approached the mean LES of the healthy control group (p=0.077). CONCLUSION: 3-D reconstruction provides an interactive, dynamic visualisation of the oesophagus, serving as a comprehensive tool for evaluating treatment response. It may contribute to refining our approach to achalasia treatment and optimising treatment outcomes. TRIAL REGISTRATION NUMBER: 22-0149.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

Towards Automated COVID-19 Presence and Severity Classification.

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.