• artificial neural networks
• B-lines
• pulmonary congestion

Extravascular lung water is a crucial parameter for the management of many different pathological conditions, especially heart failure (HF). Acute heart failure syndrome (AHFS) is a major public health problem. It is defined as a gradual or rapid change in HF signs and symptoms, which often results in an unplanned hospitalization and a need for urgent therapy. HF is the most frequent cause of hospitalization among people older than 65 years, and over the past decade the rate of hospitalization for HF
has greatly increased.
Despite new treatments and improvement in survival, hospitalizations in HF have steadily increased over the last 30 years. In patients with impending acute HF, there is a relatively long incubation period of days and weeks, characterized by lung water accumulation; in the congestion cascade, pulmonary congestion can be detected well before the appearance of
clinical signs and symptoms. Detection and treatment of hemodynamic congestion before it is clinically evident may be useful under many perspectives, the most important is that they may prevent hospitalization and progression of HF and, also, could lead to hospitalization costs reduction; in fact, this would be economically notable, since it is estimated that expenditures for HF range between 1% and 2% of the total healthcare budget.
Evaluation of the lung has been generally considered off-limits for ultrasound, since it is standard textbook knowledge that ultrasound energy is rapidly dissipated by air. This is why ultrasound is not considered useful for evaluating
the pulmonary parenchyma in physiologic conditions, where the high difference in acoustic impedance between air and surrounding tissues determines the complete reflection of the ultrasound beam and does not allow any image to be visualized. The presence of extra-vascular lung water (EVLW) or interstitial
fibrosis opens the previously locked pulmonary acoustic window, since water- or collagen-thickened interlobular septa create the adequate acoustic mismatch able to trigger the phenomenon of sonographic reverberation that generates B-lines.
B-lines (previously called ULC, Ultrasound Lung Comets) are discrete laserlike vertical hyperechoic reverberation artifacts that arise from the pleural line, extend to the bottom of the screen without fading and move synchronously with respiration. B-lines, obtained with non-ionizing, patient-friendly lung ultrasound (LUS) evaluation, have been recently proposed as an emerging echographic marker for the assessment of pulmonary interstitial syndrome, revealing the thickened pulmonary interstitium. They are present both in waterthickened pulmonary interstitium (watery B-lines), such as in pulmonary congestion due to congestive heart failure (CHF) and in collagen-thickened pulmonary interstitium (fibrotic B-lines), such as in pulmonary fibrosis and interstitial lung diseases.
The current technology for measuring EVLW can be inaccurate (chest X-ray), cumbersome (nuclear medicine and radiology techniques) or invasive (indicator dilution technique). The simple and patient-friendly way of monitoring the pulmonary interstitial syndrome by ultrasounds would be crucial in many patient populations, from CHF to dialysis, from acute lung
injury/acute respiratory distress syndrome (ALI/ARDS) to interstitial pulmonary fibrosis, since B-lines evaluation could allow a close follow-up to assess dynamic changes during pharmacological treatment and other interventions.
In less than 10 years, the proposal to use B-lines to evaluate pulmonary congestion in CHF patients has moved from the research setting to the clinical arena and has now entered recommendation papers. This application of echography is especially valuable, since it is very easy, does not require the
expertise necessary for the echocardiographic examination and interpretation, is fast to perform, portable, repeatable, non-ionizing and independent of cardiac acoustic window. However, since LUS is a sonographic technique, it suffers from ultrasound-related limitations, such as operator-dependence and lack of precise quantification.
Our aim was to develop a soft computing-based B-lines analysis for the objective, automated and quantitative classification of
the severity of the pulmonary interstitial syndrome in an operator-independent fashion. Soft computing-based models are capable to analyze complex medical data, exploiting meaningful relationships to help physicians in diagnosis, treatment and prediction of the clinical outcomes.
We developed a software which enables real-time automatic ultrasound-based classification of pulmonary interstitial syndrome and that could help health professionals for more efficient monitoring of the patient, by receiving relevant and timely information and warnings. The automated classification
of pulmonary edema is potentially the most important application of this software. The soft-computing-based B-line analysis embedded in our software is able to objectively classify the severity of pulmonary edema and pulmonary fibrosis with very high agreement over four levels of severity.
The employment of the software for the assessment of pulmonary fibrosis is also potentially of great potential impact; the possibility
of automated recognition and classification of pulmonary interstitial syndrome in patients with known pulmonary fibrosis or at high risk of developing interstitial lung disease, such as in connective tissue disease, could facilitate the diagnosis and monitoring of these debilitating conditions.
The realization of a new generation of unobtrusive monitoring interfaces as capable partners in human-oriented systems was yesterday’s fiction but today’s growing reality.
Our finding could be a good foundation for developing an integrated system for personalized monitoring devices to alert both patient and doctor about the patient’s cardio-pulmonary clinical condition. Biomedical engineering is moving towards pervasive healthcare systems in human-oriented environments exploiting ICTs. Telemedicine is being increasingly employed in many different scenarios, for patient monitoring and specialist
consultations – such as in patients with acute stroke, and to increase access for rural and underserved areas, allowing for the delivery of longitudinal and acute patient care. The development of operator-independent, computer-based systems to support
the clinical judgment is part of attempts to effect a radical change in healthcare through an overall, integrated system architecture realizing a “continuum of care” potentially everywhere; i.e., in an integrated approach addressing health management at home, in the hospital and in any nomadic environment.