Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis.

Objective. Ultrasound is extensively utilized as a convenient and cost-effective method in emergency situations. Unfortunately, the limited availability of skilled clinicians in emergency hinders the wider adoption of point-of-care ultrasound. To overcome this challenge, this paper aims to aid less experienced healthcare providers in emergency lung ultrasound scans.Approach. To assist healthcare providers, it is important to have a comprehensive model that can automatically guide the entire process of lung ultrasound based on the clinician's workflow. In this paper, we propose a framework for diagnosing pneumothorax using artificial intelligence (AI) assistance. Specifically, the proposed framework for lung ultrasound scan follows the steps taken by skilled physicians. It begins with finding the appropriate transducer position on the chest to locate the pleural line accurately in B-mode. The next step involves acquiring temporal M-mode data to determine the presence of lung sliding, a crucial indicator for pneumothorax. To mimic the sequential process of clinicians, two DL models were developed. The first model focuses on quality assurance (QA) and regression of the pleural line region-of-interest, while the second model classifies lung sliding. To achieve the inference on a mobile device, a size of EfficientNet-Lite0 model was further reduced to have fewer than 3 million parameters.Main results. The results showed that both the QA and lung sliding classification models achieved over 95% in area under the receiver operating characteristic (AUC), while the ROI performance reached 89% in the dice similarity coefficient. The entire stepwise pipeline was simulated using retrospective data, yielding an AUC of 89%.Significance. The step-wise AI framework for the pneumothorax diagnosis with QA offers an intelligible guide for each clinical workflow, which achieved significantly high precision and real-time inferences.

Protocol for a prospective study evaluating circulating tumour cells status to predict radical prostatectomy treatment failure in localised prostate cancer patients (C-ProMeta-1).

BACKGROUND: Treatment decisions in prostate cancer (PCa) rely on disease stratification between localised and metastatic stages, but current imaging staging technologies are not sensitive to micro-metastatic disease. Circulating tumour cells (CTCs) status is a promising tool in this regard. The Parsortix® CTC isolation system employs an epitope-independent approach based on cell size and deformability to increase the capture rate of CTCs. Here, we present a protocol for prospective evaluation of this method to predict post radical prostatectomy (RP) PCa cancer recurrence. METHODS: We plan to recruit 294 patients diagnosed with unfavourable intermediate, to high and very high-risk localised PCa. Exclusion criteria include synchronous cancer diagnosis or prior PCa treatment, including hormone therapy. RP is performed according to the standard of care. Two blood samples (20 ml) are collected before and again 3-months after RP. The clinical team are blinded to CTC results and the laboratory researchers are blinded to clinical information. Treatment failure is defined as a PSA ≥ 0.2 mg/ml, start of salvage treatment or imaging-proven metastatic lesions. The CTC analysis entails enumeration and RNA analysis of gene expression in captured CTCs. The primary outcome is the accuracy of CTC status to predict post-RP treatment failure at 4.5 years. Observed sensitivity, positive and negative predictive values will be reported. Specificity will be presented over time. DISCUSSION: CTC status may reflect the true potential for PCa metastasis and may predict clinical outcomes better than the current PCa progression risk grading systems. Therefore establishing a robust biomarker for predicting treatment failure in localized high-risk PCa would significantly enhance guidance in treatment decision-making, optimizing cure rates while minimizing unnecessary harm from overtreatment. TRIAL REGISTRATION: ISRCTN17332543.