Progressive hearing loss results in a decrease in hearing sensitivity and ability to understand speech. Among the different forms of progressive hearing loss, age-related hearing loss (ARHL) is the most common sensory deficit in humans, affecting communication and leading to social isolation, depression and diminishing cognitive abilities (Gates and Mills, 2005; Livingston et al., 2024). Currently, there are no treatments to prevent or cure ARHL (Wang and Puel, 2020). ARHL is a heterogeneous dysfunction, which results from the cumulative effects of ageing on the auditory system, such as cellular senescence, as well as additional intrinsic (e.g. genetic predisposition, Ingham et al. 2019) and extrinsic (e.g. environmental noise) factors. Because of this complex aetiology, the progression of the disease varies between individuals, resulting in different severity and degree of progression of hearing loss. Hearing function in clinical and pre-clinical settings can be examined through a non-invasive electrophysiological test based on the auditory brainstem response (ABR). However, the effects of hearing loss, other than an obvious increase in auditory thresholds, are often difficult to detect using ABR tests. Thus, ARHL is normally diagnosed only after patients start losing key hearing abilities, such as being unable to distinguish words in noisy conditions. This is usually an indication that some severe or irreversible damage has already happened to the sensory cells or neurons that send sound information to the brain. Therefore, as we develop therapies to target ARHL, such as gene-based replacement interventions or small molecules (Lv et al. 2024; Schilder et al. 2024), there is also a pressing need to improve the diagnostic tools to detect and predict the progression of the dysfunction at an early stage. As with any medical condition, treating a disease in its early stages increases the likelihood of successful treatment.

Machine learning (ML) techniques are increasingly being explored as tools to improve disease diagnosis and treatment (Goecks et al. 2020; Sidney-Gibbons and Sidney-Gibbons, 2019). These techniques leverage advanced algorithms to analyse large datasets, uncovering patterns that may be elusive even to well-trained experts. By identifying complex features in high-dimensional clinical data that correlate strongly with patient phenotypes, ML algorithms can be developed to predict the presence of a disease (Banerjee et al. 2023). In the auditory field, significant progress is being made in applying ML to hearing healthcare and research (Chen et al. 2021; Shew et al. 2019; Cha et al. 2019; Crowson et al. 2023; Chen et al. 2024; Erra et al., 2025), and there is a growing emphasis on the leveraging of ML-based digital tools to automate hearing assessment (Wasmann et al. 2022). However, the potential of these computational techniques to develop diagnostic tools for the early detection of progressive forms of hearing loss remains largely unexplored.

Here, we applied ML to ABR data with the goal of detecting early signs of ARHL in mice and forecasting its progression. We recorded ABRs from the commonly used C57BL/6N (6N) mouse strain and from the co-isogenic strain C57BL/6NTacCdh23+ (6N-Repaired, Mianné et al. 2016) at 1, 3, 6, 9 and 12 months of age. The 6N mice carry a hypomorphic allele of the Cadherin 23 gene (Cdh23ahl, Johnson et al. 1997; Noben-Trauth et al. 2003), which leads to progressive early-onset hearing loss starting from about 3–6 months of age. Similar to ARHL in humans (Gates and Mills, 2005), the progression of hearing loss in 6N mice begins at the higher frequencies and worsens over time, resulting in profound hearing loss by 15 months of age (Jeng et al. 2020a; 2020b; Jeng et al. 2021). In contrast, the co-isogenic 6N-Repaired strain, which are corrected for the Cdh23ahl mutation using CRISPR/Cas9 (Mianné et al. 2016), maintains better hearing than 6N mice into old age, especially for tone sensitivity for frequencies of 12 kHz and above (Mianné et al. 2016; Jeng et al. 2020b). We trained ML models through supervised learning using longitudinal ABR data as input features and genotype (i.e., mouse strain, 6N or 6N-Repaired) as target outputs. We demonstrate that, by recognising anomalies in the ABRs, the ML models were able to detect the mice with the Cdh23ahl allele in the very early stages of ARHL. This approach was validated on unseen data from two independently acquired datasets, demonstrating the broad validity and generalisability of our conclusions. Finally, we used ML to forecast the future progression of the hearing capabilities of young adult mice up to 1 year of age. This work highlights the benefit of using ML for the early diagnosis of ARHL, providing a foundation for future studies exploring its applicability to human datasets.