Pearl's Second Opinion can help you make sense of your 3D data
A few years ago, owning a CBCT machine was a sign that your practice was ahead of the curve. Today, it’s increasingly common. But while access to 3D imaging has grown, the ability to meaningfully interpret and act on that data hasn’t always kept pace.
That’s the paradox: we’re generating more clinical information than ever—but often struggling to make full use of it.
Unlike 2D radiographs, which most general dentists are trained to read from early in their careers, CBCT scans introduce a steep learning curve. A single scan can contain hundreds of slices, revealing intricate anatomical details from all angles. But without specialized radiologic training—or the time to comb through every view—valuable findings can be overlooked.
CBCTs offer immense clinical value—but they also demand a new kind of literacy. That’s a daunting prospect for many general practitioners who have made a major investment in 3D technology, but aren’t sure if they are using it to its full potential.
Now, however, AI-powered segmentation is catapulting dentists to the top of the learning curve.
By automatically identifying and labeling critical anatomical structures—like the mandibular canal, sinuses, or dentition—segmentation tools like Second Opinion 3D help distill a mountain of data into digestible, clinically-relevant visuals. Instead of sifting through endless slices, you can focus directly on the structures that are relevant to your case.
Whether we’re planning a complex extraction, mapping out an implant site, or educating a patient, the ability to isolate and highlight anatomical features is powerful.
2D radiographs are fast, familiar, and fine for many routine assessments—but they fall short when it comes to complex anatomy or subtle pathology. With CBCT, we gain a level of visibility that 2D simply can’t offer.
In CBCT imagery, we can see periapical lesions that would otherwise be hidden behind thick cortical bone, visualize the full architecture of a root canal system before ever picking up a file, and evaluate the exact position of an impacted third molar relative to the inferior alveolar nerve. In cases of trauma or unexplained pain, 3D imaging can reveal vertical root fractures or bone defects that aren’t evident on traditional films.
But even more importantly, CBCT—especially when paired with segmentation—helps us when a referral is called for. When something looks atypical or unclear, segmented scans can surface features that warrant further investigation, flagging cases where a formal radiologic review is appropriate. That kind of clinical clarity not only reduces missed diagnoses—it ensures patients get to the right specialist, at the right time, for the right reason.
Whether it’s planning a sinus lift, investigating a non-healing site, or evaluating pathology beyond your comfort zone, segmentation helps clinicians see more, decide faster, and refer more confidently.
As with 2D radiologic AI, a key additional benefit of 3D radiologic segmentation is better patient conversations.
We’ve long known that visuals help patients grasp their diagnosis and treatment options. But, until recently, the value of showing 3D scans to patients was mostly the “wow” factor. After all, without the ability to quickly isolate anatomical structures and clean up the view for maximum legibility, patients have more grayscale visual data to confuse them than they do when looking at 2D imagery.
When scans are segmented, labeled and color-enhanced—as they are in Second Opinion 3D— they transform from an internal diagnostic tool into a shared visual language. Patients can now see their anatomy, understand what’s being treated, and ask informed questions. It’s a shift that builds trust—and often increases treatment acceptance as a result.
Some will still see AI segmentation as a premium add-on. I’d argue it will be the baseline.
As 3D imaging becomes more integrated into everyday dentistry, clinicians need tools that make that data manageable, actionable, and meaningful—not just for specialists, but for anyone delivering comprehensive care.
Today, segmentation does that by enhancing visualization. Tomorrow, it will do even more—supporting pathology detection, streamlining referrals, and helping us diagnose with even greater precision.
But even now, it’s clear: better 3D understanding starts with better 3D visibility.