Artificial intelligence advancement in addressing cough

Table 1 Etiology of cough[4-6]

Acute cough (< 3 weeks)

Respiratory tract infections (pharyngitis, tonsillitis, bronchitis, pneumonia)

Subacute cough (3-8 weeks)

Post-infectious cough

Chronic cough (> 8 weeks)

Upper airway cough syndrome

Asthma (cough-variant asthma)

Eosinophilic bronchitis

Gastro-esophageal reflux disease

Drug induced (angiotensin converting enzyme inhibitor)

Chronic obstructive pulmonary disease

Bronchiectasis

Environmental and behavioral factors (smoking, pollution)

Neuronal hypersensitivity

Table 2 Major breakthroughs in artificial intelligence integration

Target	Data type	AI technology	Findings
Screening tool for TB, COVID-19, pneumonia	Cough sound	AudibleHealth AI (RAISONANCE)[86]	Disease	Sensitivity	Specificity
			Tuberculosis	90%	72%
			COVID-19	97%	75.5%
			Pneumonia	94%	73%
Muti-disease smartphone diagnosis	Cough sound	ResAppDx-EU[87]	Respiratory disease	Sensitivity	Specificity
			Pediatric	83%-97%	82-91%
			Adult	83%-89%	84%-91%
Automated cough analysis	Cough sound	Convolutional recurrent neural network[88]	97.2% accuracy; 92.9% sensitivity; 97.6% specificity
AI cough classifier	Cough sound	Machine learning (mobile based cough sound classifier)[43]	Detection of respiratory diseases (TB, asthma, COPD) in rural Tanzania
Chronic cough identification	Cough with electronic health record data	XG boost model[89]	ROC-AUC of 0.916. Best performance when compared with other machine learning algorithms based on logistic regression and neural network approaches
Lung sound analysis	Lung sound	Feelix smart stethoscope (Sonavi Labs)[90]	Advantages over traditional stethoscope: Quick auscultation since precise positioning is not required; active noise cancellation; automatic abnormality detection
Bio-acoustic foundation model	Cough, speech & breath sound	Google HeAR (health acoustic representations)[91,92]	HeAR outperformed several audio encoder baselines such as TRILL, FRILL, BigSSL-CAP12, and CLAP by achieving highest overall performance across 33 tasks. Ranked as the top model in health acoustic detection while dominating cough inference and spirometry achieving the highest mean reciprocal rank (0.708)
Pattern classification and severity mapping	Cough sound	Swaasa platform[66]	90% accuracy
Monitoring tool	Cough sound	Hyfe cough monitor[93]	90.4% sensitivity (supports from initial screening to patient management)

AI: Artificial intelligence; CLAP: Contrastive language audio pretraining; COPD: Chronic obstructive pulmonary disease; COVID-19: Coronavirus disease 2019; HeAR: Health acoustic representations; TB: Tuberculosis; SSL: Semi-supervised learning.