Comparing current knowledge to Jihva Roga's clinico-etiopathological notions (diagnosing tongue disorders)

Abstract

Aim of the study:

The purpose of this study is to systematically compare Jihva Roga's clinico-etiopathological concepts with contemporary medical understandings of tongue disorders, such as Upajihva (cystic swelling) and Kaphaja Jihva Kantaka (chronic leukoplakia), in order to identify potential diagnostic and therapeutic synergies. This technique aims to bridge the gap between historical diagnostic paradigms, such as those found in traditional Chinese medicine and Ayurveda, where tongue examination has long been used as a key diagnostic tool, and modern medical science.

Methodology: This study will compare the clinical descriptions and etiological components of Jihva Roga found in classical Ayurvedic writings to modern medical diagnostic criteria for illnesses such as cystic swelling, sublingual abscess, chronic leukoplakia, and acute glossitis. This comparative analysis will focus on how conditions such as Upajihva (cystic swelling), Alasa (sublingual abscess or carcinoma), Kaphaja Jihva Kantaka (chronic leukoplakia), and Pittaja Jihva Kantaka (acute glossitis/atrophic glossitis/red glazed tongue) are envisioned and managed in both systems.

Results: The study found substantial similarities between Jihva Roga's descriptions and modern medical classifications of tongue diseases, particularly in symptom presentation and assumed underlying pathology. For example, the description of Kaphaja Jihva Kantaka, described in Ayurvedic scriptures as thorny outgrowths, is similar to the hyperkeratotic plaques found in chronic leukoplakia, a modern clinical entity. Similarly, in Ayurvedic literature, Upajihva is characterised by cystic swelling, which corresponds to the clinical presentation of sublingual cysts or ranulas, demanding a multimodal diagnostic approach for effective identification and treatment.

Conclusion:

This comparative study also highlights the possibility for combining traditional diagnostic expertise with new technologies such as dermoscopy and machine learning to improve the accuracy of early detection for oral mucosal diseases.