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Thesis
Periodontal phenotype survey
University of Iowa
Master of Science (MS), University of Iowa
Spring 2026
DOI: 10.25820/etd.008444
Abstract
BACKGROUND
The periodontal phenotype plays a definitive role in the esthetic and functional outcomes of restorative and surgical dental therapies. Historically, patients have been categorized into binary groups (thick-flat or thin-scalloped) based largely on visual assessment. However, the reliability of utilizing visual architecture to predict actual gingival thickness remains questionable.
OBJECTIVES
The primary aims of this cross-sectional study were to classify distinct periodontal phenotypes in the maxillary anterior zone, determine their prevalence, and statistically evaluate the correlation between anatomical architecture (tooth form and scalloping) and gingival thickness.
METHODS
A total of 103 periodontally healthy adult subjects were enrolled. Clinical gingival thickness was assessed on maxillary anterior teeth (#9, 10, 11) using the Probe Transparency (PPV) method (categorized as Thick or Thin). Digital intraoral scans (IOS) were utilized to measure Clinical Crown Width/Length (CW/CL) ratios and Papilla Height. Unsupervised machine-learning clustering (HDBSCAN with KNN assignment) was performed to objectively identify anatomical morphotypes.
RESULTS
A statistically significant inverse correlation was observed across all teeth between CW/CL ratios and papilla height (p<0.05). Machine learning identified three distinct architectural clusters for central incisors and canines (Square-Flat, Slender-Scalloped, and a Hybrid Square-Scalloped group), and four clusters for the highly variable lateral incisors. Crucially, cross-referencing these clusters with PPV data revealed a significant "phenotypic disconnect." While "Square-Flat" morphotypes predictably exhibited "Thick" tissue, the "Slender-Scalloped" morphotypes demonstrated a highly mixed distribution, with a substantial portion of subjects presenting with a "Thick" gingival phenotype despite a highly scalloped, slender architecture.
CONCLUSION
Gingival architecture (visual tooth form and scallop) and true gingival thickness (translucency) are independent clinical variables. A highly scalloped architecture does not guarantee a thin tissue phenotype. Therefore, visual assessment alone is an insufficient diagnostic tool. Clinicians must utilize a multi-variable "Phenotype Matrix"-combining digital morphometrics with probe transparency testing-to accurately assess surgical and restorative risk in the esthetic zone.
Details
- Title: Subtitle
- Periodontal phenotype survey
- Creators
- Austin S. Taylor
- Contributors
- Sukirth Ganesan (Advisor)Joseph Kan (Committee Member)Georgia Johnson (Committee Member)Shareef Dabdoub (Committee Member)Sandra Guzman-Armstrong (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Oral Science
- Date degree season
- Spring 2026
- DOI
- 10.25820/etd.008444
- Publisher
- University of Iowa
- Number of pages
- xii, 64 pages
- Copyright
- Copyright 2026 Austin S. Taylor
- Language
- English
- Date submitted
- 04/20/2026
- Description illustrations
- Illustrations, graphs, charts, tables
- Description bibliographic
- Includes bibliographical references (pages 61-64).
- Public Abstract (ETD)
The shape and thickness of a person’s gums, known as their "gum phenotype", play a major role in the success of dental treatments, such as implants, braces, or gum surgeries. If a patient has thin gums, they are at a much higher risk for complications like gum recession (where the gums pull away from the teeth) following dental work.
For decades, dentists have relied on a general rule of thumb. If a patient's teeth are long and narrow with highly "scalloped" (curved) gum lines, their gums are assumed to be thin and delicate. Conversely, if the teeth are short and square, the gums are assumed to be thick and strong.
This study aimed to test if this historical assumption is actually true. We examined 103 healthy adult volunteers, using advanced 3D digital scanners to measure the exact shape and proportions of their front teeth and gums. We then safely measured the actual thickness of their gums using a small dental instrument. Finally, we used a machine-learning computer algorithm to group the patients based on their measurements without any human bias.
The computer revealed that human anatomy is more complex than just two simple categories, identifying several "hybrid" groups. Most importantly, the study proved that looks can be deceiving. We found that many patients who had long, delicate-looking teeth actually possessed very thick, strong gums.
Ultimately, this research proves that a dentist cannot accurately guess how thick a patient's gums are just by looking at the shape of their teeth. To provide the safest, most effective, and best-looking dental treatments, dentists must physically measure gum thickness rather than relying on visual guesswork.
- Academic Unit
- Oral Pathology, Radiology and Medicine
- Record Identifier
- 9985177176002771
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