Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan

Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction

Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.

Quantitative Computed Tomography and Response to Pronation in COVID-19 ARDS

BACKGROUND

The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative computed tomography (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO2 clearance and those who did not.

METHODS

This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the PaO2 /FIO2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs.

RESULTS

One hundred twenty-five subjects were enrolled, of which 116 (93%) were O2 responders and 51 (41%) CO2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99).

CONCLUSIONS

Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO2 in mechanically ventilated subjects with COVID-19-related ARDS.

Monitoring lung recruitment

PURPOSE OF REVIEW

This review explores lung recruitment monitoring, covering techniques, challenges, and future perspectives.

RECENT FINDINGS

Various methodologies, including respiratory system mechanics evaluation, arterial bold gases (ABGs) analysis, lung imaging, and esophageal pressure (Pes) measurement are employed to assess lung recruitment. In support to ABGs analysis, the assessment of respiratory mechanics with hysteresis and recruitment-to-inflation ratio has the potential to evaluate lung recruitment and enhance mechanical ventilation setting. Lung imaging tools, such as computed tomography scanning, lung ultrasound, and electrical impedance tomography (EIT) confirm their utility in following lung recruitment with the advantage of radiation-free and repeatable application at the bedside for sonography and EIT. Pes enables the assessment of dorsal lung tendency to collapse through end-expiratory transpulmonary pressure. Despite their value, these methodologies may require an elevated expertise in their application and data interpretation. However, the information obtained by these methods may be conveyed to build machine learning and artificial intelligence algorithms aimed at improving the clinical decision-making process.

SUMMARY

Monitoring lung recruitment is a crucial component of managing patients with severe lung conditions, within the framework of a personalized ventilatory strategy. Although challenges persist, emerging technologies offer promise for a personalized approach to care in the future.

Lung Imaging and Artificial Intelligence in ARDS

Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.